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PASSIVITY/LYAPUNOV BASED

CONTROLLER DESIGN FOR

TRAJECTORY TRACKING OF

FLEXIBLE JOINT MANIPULATORS

Imm

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by

Pierre Sicard, John T. Wen and Leonardo Lanari

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Rensselaer Polytechnic Institute

Electrical, Computer, and Systems Engineering Department

Troy, New York 12180-3590

August 1992

CIRSSE REPORT #121

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Passivity/Lyapunov Based Controller Design for

Trajectory Tracking of Flexible Joint Manipulators

Pierre Sicard', John T. Wen* and Leonardo Lanari'"

* NASA Center for Intelligent Robotic Systems for Space Exploration

Rensselaer Polytechnic Institute, Troy, NY 12180-3590, USA

** Dipartimento di Informatica e Sistemistica

UniversitY, di Roma "La Sapienza", Via Eudossiana 18, 00184 Roma ITALY

Abstract

A passivity and Lyapunov based approach for the control design for the trajectory tracking problem of

flexible joint robots is presented. The basic structure of the proposed controller is the sum of a modelbased feedforward and a model-independent feedback. Feedforward selection and solution is analyzed

for a general model for flexible joints, and for more specific and practical model structures. Passivity

theory is used to design a motor state-based controller in order to input-output stabilize the error system

formed by the feedforward. Observability conditions for asymptotic stability are stated and verified. In

order to accommodate for modeling uncertainties and to allow for the implementation of a simplified

feedforward compensation, the stability of the system is analyzed in presence of approximations in the

feedforward by using a Lyapunov based robustness analysis. It is shown that under certain conditions,

e.g. the desired trajectory is varying slowly enough, stability is maintained for various approximations

of a canonical feedforward.

1

1 Introduction

Joint flexibility is well recognized for its adverse effect on stability and performance of robotic manipulators [23, 28]. The main implications of joint flexibility are that the number of degrees of freedom is larger

than the number of inputs, and that the system is not passive from the torque input to the link velocity

as for rigid robots such that most of the control schemes designed for rigid robots are inappropriate

for the control of flexible joint robots. Different approaches have been considered to solve the problem

of controlling robots with joint elasticity including singular perturbation techniques, exact linearization

and passivity based design. Singular perturbation techniques and exact linearization generally require

linear spring assumption and exact knowledge of the system parameters, and are characterized by their

computational complexity and their lack of robustness to parameter uncertainty (for a summary, see

[27]). Furthermore, exact linearization requires zero gyroscopic force, and the feedforward compensation

for linearization and the feedback stabilization are intertwined and errors in the feedforward may affect

the closed loop stability in an adverse way.

The concept of passivity is traditionally defined as an input/output condition describing a class of physical

systems that do not generate energy [18]. This property has been used in the feedback stabilization for

fully actuated rigid robots [29], satellites [6], and flexible joint robots [4, 31]. The passivity property for

flexible joint robots (motor torque and motor velocity form a passive pair) was recognized in [4] and was

used in a proportional-derivative (PD) type controller design. The method requires inherent damping in

both links and motors. Similar results without requiring the inherent damping have recently appeared

in [31]. The PD controller has been generalized to a general passive controller in [22]. This method also
requires inherent damping and furthermore, linear spring assumption. Moreover, a frequency analysis

that yields a non-causal solution is used to find the feedforward. The design of general passive controllers

without the requirement of inherent damping and of the linear spring assumption, and that uses causal

feedforward was presented in [15, 24]. This approach allows to consider both the set point and tracking

problems, and does not require a large elastic coupling stiffness as singular perturbation techniques does.

The basic structure of the proposed controller is the sum of a model-based feedforward and a modelindependent feedback [15, 24]. The feedforward design in the proposed scheme is very similar to the

exact linearization approach, both essentially solve an inverse plant problem, but requires much less

model information in the set point control case, and the additive separation between the feedback and

feedforward implies that errors in the feedforward do not lead to instability. However, the closed loop

performance cannot be arbitrarily assigned and the feedforward for the tracking problem may be complex.

We address the issue of computing a causal feedforward for a nonlinear spring.

Passivity based controllers have been recognized for their robustness to parameter uncertainties [1, 31].

This robustness may allow to simplify the control law for implementation as opposed to exact linearization

techniques for which simplifications may compromise stability. We will analyze the stability of the system

under feedforward approximations.

This paper addresses the following trajectory tracking problem for flexible joint robots:

Given the desired output trajectory described by Od(t) and its derivatives, t > to, design a feedback

control law u(t) so that 0(t) tracks an(t) in some sense while assuring internal stability of the system.

In Section 2, background information on passivity, and useful definitions and theorems are provided. In

Section 3, the model of the class of systems that is considered and some of its properties are presented.

The proposed approach for controller design which exploits the passivity property of flexible join t robots

is exposed in Section 4. In Section 5, various possible forms of feedforward for the trajectory tracking

problem are presented and the issue of solving the feedforward is addressed. In Section 6, the controller

design is carried out for particular choices of feedforward, referred to as the canonical feedforward compensation schemes, under the assumption that certain conditions are satisfied. Zero-statedetectabili_ty

of the canonical feedforward compensation schemes is analyzed in Section 7. In Section 8, the stability

of different possible forms of feedforward is analyzed by using a Lyapunov based robustness analysis.

Conclusions are drawn and future work is summarized in Section 9.

2 Passivity and definitions

The notion of passivity of an input-output system, motivated by the dissipation of energy across resistors in an electrical circuit, has been widely used in order to analyze stability of a general class of

interconnected nonlinear systems, e.g. [32, 34]. Passivity was also studied for state-space representations

of nonlinear systems, allowing a more geometric interpretation of a0t!ons such as available, stored, and

dissipated energy in terms of Lyapunov functions [38, 39, 40]. This point of view has been specifically

developed in [10, 11], and leads to Lyapunov-theoretic counterparts to many stability results developed

within an input-output perspective, as well as to a nonlinear form of the Kalman-Yacubovitch-Popov

lemma. The great interest of considering passive (strictly passive) systems lies in the fact that passivity

is invariant under feedback connections and that passive systems are always stable, and under additional

assumptions asymptotically stable [17, 18, 38, 41].

In [18], necessary and sufficient conditions for a nonlinear autonomous system with the control entering

linearly to be passive are given. It is well known that passive linear systems are necessarily minimum

phase and, conversely, a minimum phase plant with relative degree zero or one can be rendered passive

via a static state feedback. A similar relationship for nonlinear systems has recently been published [5].

This allows to extend the class of systems to which the stability results for passive systems applies.

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We now present definitions and theorems related to passivity and to the stabilization of passive systems.

The information presented here was mainly drawn from [5, 10, 11, 18, 33, 38, 39, 41, 42, 43]. Other

papers of interest that treat positive realness, which is a subcase of passivity, are [2, 12, 37].

Definition 2.1 [33] A function f : R+ ---*R a is an element of the vector space Lp(R+, R a) if

j_ If(t)lp dt < c_ for p E [1, _)

Define the input and output signal spaces, He, Y_, respectively, as the extended spaces L2e(R+, R.a), i.e.

the causal extension of L2(R+, R n) :

L2e(R+, R") -_ {f: R+ --* R '_ [ PTf E L2(R+, p.n), VT E R)

where PT is the projection operator that truncates f at T:

A f f(t) fort<T

(PTf)(t)

= l 0 otherwise

Define the truncated inner product by

(u(.), v(.)) T = (PTu('), PTV('))2 = ff (PTu(t))rpTv(t) dt

w

= :

m

By a dynamical system, we mean an I/O mapping H : L/_ _ Y_. The following definitions are given for

He, Ye being extended Lp-spaces, but we will use uniquely L2-spaces in the remaining of the report.

Definition 2.2 [33] For p E [1, oo), the function [].[]p : Lp[0, c¢) -- [0, o0) is defined by

IIf(-)llp = (_o¢_If(t)l p dt] _

The function I1"11oo : L¢0[0, oo)--, [0,_) is defined by

IIf(')ll_ = ess sup If(t)l

te[0,oo)

Definition 2.3 [33] A dynamical system H is Lp-stable if

(i) y E Lp whenever u E Lp, and

(ii) There exist finite constants k, b such that

I]yll_<-k Ilull_+ b Vu E Lp

Definition 2.4 [33] A dynamical system H is Loo-stable (Bounded Input-Bounded Output-stable, BIBO

stable) if

(1) y E Lp whenever u E Lp, and

(il) There exist finite constants k, b such that

Ilyll_ -<k Ilull_ + b Vu e Z_

3
Differentdefinitionsfor passivity,whichcharacterizessystemsthat donotgenerateenergy,havebeenused

[43].Wewill adoptoneof thesedefinitions,but first will definethe moregeneralconceptof dissipativity

[10,111.

Definition 2.5 [11] A dynamical system H is dissipative with respect to the triplet (Q, S, R) if

w(u, y) = (y, QY)T + 2 (y, Su)T + (u, RU)T >_ 0

for all T > 0 and u E Lie, where w(u, y) is defined as the supply rate, and Q, S and R are memoryless

bounded operators with Q and R self-adjoint.

Definition 2.6 [43] A dynamical system H storing no energy at t = 0 is passive if for all T E R+ and

all admissible pairs {u(.), y(.)} E {He, Y_},

(y,u)T _ 0

The quantity (y, U)T is sometimes defined as the input energy of the system [18]. This nomenclature will

be used here.

Definition 2.7 [38] A dynamical system H storing no energy at t = 0 is strictly passive if for all T E R+

and all admissible pairs {u(.), y(.)) E {U¢, y_}, H - _I is passive for some real constant _ > O, i.e. if

(y,u)r - e > o

Hence, a finite-gain I/O stable system is dissipative with respect to (-I, 0, k2I), while a passive system is dissipative with respect to (0, 1⁄2I, 0) and a strictly-passive system is dissipative with respect to

(0,1⁄2I,-e) [11]. This definition of strict-passivity corresponds to the definition of U-strong-passivity

(USP) in [10]. Two additional forms of strong passivity as defined in [10] are Y-strong-passivity (YSP)

which corresponds to systems that are dissipative with respect to (-x, _I,1 0) for some real constant

E > 0, and Very-strong-passivity (VSP) which corresponds to systems that are dissipative with respect

to (-_1,-_I,-_2) for some real constants sl > 0 and _2 > 0.

In [38, 39], different energy quantities are defined. The required energy E_ is the energy needed to excite

a system to a given set of initial conditions. The available energy E_ is the maximum energy that can be

extracted from a system. The cycle energy Ec is the minimum energy it takes to cycle a system between

the equilibrium and a given state. Under mild conditions, these quantities are well defined for a passive

system :

Lemma 2.1 [38] Consider a realization of a passive system and assume that the state space is reachable.

Then E,, E, and Ec exist (i.e. E_, E_, Ec < _) and are nonnegative. .Moreover, 0 < E_, E¢ < E_.

In principle, these quantities can be used to construct Lyapunov functions as illustrated by the following

theorem.

Theorem 2.1 [39] The set of passible storage functions of a dissipative dynamical system forms a convex

set. Hence, aE_ + (1 - a)E_, 0 g a _< 1, is a possible storage function for a dissipative dynamical system

whose state space is reachable from the point in space where the storage function attains its minimum.

An important characteristic of the systems studied here is that they satisfy the following conservation of

energy equation [17, I8]:

Input energy = Final energy - Initial energy "+ Dissipated energy

where, for passive systems, the dissipated energy is always nonnegative.

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In [10], a necessary and sufficient condition for a nonlinear system with the control input entering Hnearly

to be dissipative is given. The test involves the construction of some functions, including a function

representing the generalized energy of the system. This function is positive for dissipative systems such

that this condition can be used as an aid for the construction of a Lyapunov function for the system.

Input/output and state space stability and stabilization of passive systems have been extensively studied

, and we now present some of these results, but first provide two more definitions.

Definition 2.8 A dynamical system H is said to be zero-state detectable if u(t) --- 0 and y(t) =_ 0 imply

that the state x(t) = O.

For linear systems, this corresponds to observability.

We introduce here a weaker form of zero-state detectability.

Definition 2.9 A dynamical system H is said to be weakly zero-state detectable if u(t) = 0 and y(t) -- O

imply that the state x(t) --* 0 asymptotically.

There is no consensus actually in the literature about the nomenclature regarding zero-state detectability.

Hence, in [5], Definition 2.8 corresponds to observability, and Definition 2.9 to zero-state detectability.

Theorem 2.2 [10] Consider a dynamical system H which is dissipative with respect to the triplet (Q, S, R)

and zero-detectable. Then, the system with zero input is Lyapunov stable if Q < 0 and asymptotically

stable if Q < o.

Hence, passive and strictly-passive (USP) systems are stable while YSP and VSP systems are asymptotically stable.

The same conclusions hold for weakly zero-detectable systems.

An important characteristic of passivity is that it is invariant under feedback connections [39, 41]. Hence,

a feedback system consisting of a passive dynamical system in both the feedforward and feedback loop

is itself passive and thus stable. Moreover, the sum of the stored energies in the forward loop and in the

feedback loop is a Lyapunov function for the closed loop system. Hence, the Lyapunov function used to

show passivity of the two system components may be used as part of the overall Lyapunov function used

to show the stability of the system. This procedure to show stability is formalized in [41].

We now state a simplified version of a very important theorem in the stabilization of passive systems,

the Passivity Theorem, which provides sufficient conditions to determine stability of the interconnection

of systems.

Theorem 2.3 Passivity Theorem [9]: The system formed by the negative feedback connection of a passive

dynamical system and of a strictly passive dynamical system with finite gain is L2-stable.

Hence, any passive system can be rendered L2-stable by closing a strictly passive loop with finite gain.

I/O stability infers internal state space asymptotic stability if the closed-loop system is stabilizable and

zero-state detectable (if these properties hold globally, the internal stability is also global). Hence, under

observability (zero-state detectability) and reachability conditions, the interconnection of a passive and

a finite gain strictly passive system is asymptotically stable [38, 41]. Also, it is sometimes possible to

show via a Lyapunov type argument that y(t) ---, 0 asymptotically if u(t) = O. Then the zero-state

detectability alone guarantees internal asymptotic stability.

It is well known that passive linear systems are necessarily minimum phase and, conversely, a minimum

phase plant with relative degree zero or one can be rendered passive via a static state feedback (see [5]

for a particular form of this statement). A similar relationship for nonlinear systems has recently been

published in [5]. It is shown that a nonlinear system can be rendered passive via static feedback, i.e. it

5
is feedbackequivalento a passivefeedback,if andonlyif thesystemis weaklyminimumphaseand the

relativedegreeis one(seeAppendixB for the formalstatement).An equivalentstatementfor this is

that the systemis asymptoticallystabilizablebystatefeedback[5]. Hence,the classof systemsto which

stabilizationby passivityapproachappliescanbeextendedby usingthis result.

3 Modeling of flexible joint manipulators

Thedynamicalequationof motionfor flexiblyjointedmanipulatorswith rigid links canbewritten as:

M(0)0 + C(0,0)0+ D(_))+ 46) = Bu (3.1)

r($) = g(8) + k(8) (3.2)

where 0 E R n is the displacement vector, u E R m is the input force vector, M is the mass-inertia matrix,

D is the viscous damping and Coulomb friction, C corresponds to the centrifugal and Coriolis forces, g is

the gravity force, and k represents the spring coupling force. Note that in model (3.1), it is assumed that

no external force is exerted on the manipulator, and that joint and link friction is position independent.

We will consider maniTPulators with all its joints exhibiting flexibility and with each link being actuated,

i.e. 8 = [ 02 OmT ] where 0t, 0,,_ E R m are respectively the link and motor displacement vectors, and

n = (2. m). These assumptions are not essential but will allow to obtain a more concise presentation.

We define the output of interest as

y = h(O) (3.3)

Usually y is a function uniquely of the link state.

3.1 Properties of the system

We now state some properties of the system that will be useful in this paper.

c(o, o)o_ _._(o,o) - -_ oo ° "

_/is Used to denote the derivative of M(O) along the solution, i.e:

}_I(8, 0) _= £ OM(O)o, (3.4)

While C(0, 0)0 is unique, C(0, 0) is not. However, there is a close relationship between C(8, O) and M(9):

for any choice of C(0, t_) and for any z E R ",

z (_M(O,z)- C(O,z)) z = O

Two frequently used representations for C will be considered.

Representation of C using Christoffel's symbols : In this representation,

rt

£x

Ckj -- E ¢iJk(O)Oi

i=l

1 [OMkj OM_ OM_j O,

= + i=1

For this representation, and this representation only, (1⁄23J(O,z)- C(O,z)) is skew-symmetric [21].

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Representation of C using MD-notation : First define a matrix MD that depends on two vector

arguments:

n OM(O) T

M_(O,_) _=F_,--SV/-_"_, (3.._)

i=l

where ei is the i th unit vector in R '_. MD and /1;/ are related as follows:

MD(O,_)w = _t(O, _)_ (3.6)

MD also satisfies

MT(O,v)w= MT(O,w)v

C(O, O) can be expressed succinctly as

C(O,O) _Mr(O,O) _-M_(O,O)-

(3.7)

(3.8)

The Coriolis and centrifugal coefficient matrix C will be represented by C to denote an arbitrary choice

of representation unless noted otherwise, by Cc to denote the representation by the Christoffel's symbols,

and by CD to denote the representation by the MD-notation.

Also, it is always possible to find a vector function f(0) such that r(0) can be factored as

r(0) = _(0)+RO (3.o)

for any user defined constant matrix R E R nxn. R will be represented as

R = R21 R_2

where Rll, R12, R21, R22 E R "_x''.

We will also represent r as

where rl, r2 E R _nxa. Similarly,

where rx,r2 E R taxi.

r(0)= [ fl(0)If2(0)

The friction term D can also be represented by the sum of a linear component D0 and of a nonlinear

term DI as follows

D(O) = Dot_ + DI(O)

3.2 Structure of the model

It is well recognized that the control of fle.,dble joint robots is highly dependent upon the structure of the

model, which depends on various factors including the kinematic arrangement of the finks and the way

motors are mounted [7, 14, 20, 27, 35]. The following standard modeling assumptions have been used in

the literature [7, 20, 26, 27, 30] :

• Linear spring assumption.
• Motor inertia is symmetricaboutmotoraxisof rotation(M andgravity forcesonly dependoil 0e).

• Kinetic energy of each motor is due mainly to its own rotation, or motion of motor is a pure rotation

with respect to an inertial frame (Neglect gyroscopic effects; M12 = M T = 0).

None of these assumptions will be used a priori. However, we present here the structure of the model

that is obtained under some standard assumptions and for specific manipulator structures.

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3.2.1 General model

For a general flexible joint manipulator, equation (3.1) can be expanded as [8, 20]:

M (Oe,Or,,) J [ L,, ] +

C21(Oe, Om,_t,_m) C_(Ot, O,_,Oe, Om) dm +

[ D..(0m)] + [ rl(0,, o,,,) 0

u (3.10)

r,(St, 0m) [ ge(Se, Sm) + (3.11)

= [

where St, era E l:t '_ are the link and motor displacement vectors respectively with elements numbered

from base to tip, link m being the last link; u E 1_m is the input force vector; Mll is the mass-inertia

matrix of the links including the mass of the motors mounted on the links as if they were rigidly attached

to the links; M22 is the motor inertia matrix; M12 represents the coupling or interaction matrix that gives

the dynamic coupling between the motor and link accelerations; M T is the counterpart that gives the

interaction between the link and motor accelerations; De and D,,, are respectively the torques due to link

and motor viscous and Coulomb friction (the effect of friction in the transmission element is neglected);

Cll and C12 represent the coefficients of the torque acting on the link shafts due to centrifugal and

Coriolis forces; C T and C22 represent the coefficients of the torque acting on the motor shafts due to

centrifugal and Coriolis forces; ge and gm are the gravity forces acting respectively upon the link and

motor shafts; kl represents the spring coupling forces; N is the matrix of gear ratios; and 0, [ E R m×m

are respectively the zero and the identity matrices.

3.2.2 M12 nonsingular

This condition may arise when the stator of motor i, i = 1,...,m, is mounted on the llnk it drives

(rotor elastically coupled to link i - 1), or on a link higher in the chain, i.e. on a link ji, ji > i, for

certain kinematic configurations such as planar robots with the axis of rotation of the motors and links

all coplanar. The system is then represented by the same equations as the general model, i.e. (3.10,

3.11).

Subcase : Symmetric motor |nert[a : : :

If the inertia of the motors is symmetric about their axis of rotation, then (3.10,3.11) become

=: T:

D,_(Om) + r_(O,:,Om)

+

= [o] [ u (3.12)

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r2(Oe,0m) -kl(0e, 0m) ]

(3.13)

3.2.3 M12 strictly upper triangular

The matrix MI2 may be singular but non-zero when the energy of at least one motor is due to its own

rotation. Also, a common configuration yielding this condition is when the stator of motor i is mounted

on link i - 1 while its rotor is elastically attached to link i for i = 1,...,m (the stator of motor 1 is

mounted in the inertial frame). In a more general case, the stator of motor i is mounted on llnk ji while

its rotor is elastically attached to link i for ji < i and i = 1,.--, m (This leads to M12 = 0 if all the

motors are mounted in the inertial frame). It has been independently pointed out in [19] and [31] (the

former is for the exact case) that for that case, M12 is strictly upper triangular.

We now present the structure of the model with symmetric motors for MI_ strictly upper triangular

where M12 is state dependent or constant.

Subcase 1 : Symmetric motor inertia and M12 strictly upper triangular

In this case, the system dynamics are described by (3.12, 3.13) with the Coriolis and centrifugal matrix

having the following structure:

c(oe, = [CAl(Oe, Oe)+C_(Oe, Om) cA2(Oe, o Oe)]

where CA is the Coriolis and centrifugal term for:the rigid robot and the structure of the other terms is

described below (see Appendix A for the complete set of equations).

It was pointed out in [31] that M12 has the following structure:

0 ml,2(0_,1) rnl,z(Oea,Ot,2) "'" ml,_(Ota,"',Ot,,,,-1)

0 0 m2,a(Oe,2) ... m2,,_(Oe,2,"',Oe,m-1)

M12(0t) = : : : : :

0 0 0 "" 0

where ml,j are scalar functions. This particular structure for M12 yields that (Appendix A)

• C_ is skew-symmetric with row m. equal to zero and row i independent of any O,,,,j, j <_

i= 1,.--,m-I,

• CA is strictly upper triangular,

• C A is strictly lower triangular.

i,

Subcase 2 : Symmetric motor inertia, and Ml_ constant and strictly upper triangular

Matrix M12 may be constant due to the configuration of the manipulator and due to the position (orientation in particular) of the motors on the links. This is the case when the a.,c_isof rotation of each motor

is coplanar with the axis of rotation of the llnk that it drives, and that the axis of rotation of the links

are mutually perpendicular (possible for up to three links, unless some of the motors are mounted in the

inertial frame yielding weaker constraints and allowing for a larger number of links).

In this case, the system dynamics are described by

D.,(O_) ] + M T M22 0 0
[ rl(Oe, r2(Oe, Om) 0,,) 1

r2(O_,Ore) gev e) -k_(Oe, Om)

where Cn is the Coriolis and centrifugal term for the rigid robot.

3.2.4 M12 : 0

= [o] I u (3.14)

(3.15)

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There is no gyroscopic coupling when the energy of each motor is due to its own rotation, e.g. the motors

are mounted in an inertial frame, or all the motor axis are perpendicular to the llnk axis [20].

In this case, the dynamics of the system are described by

0 M=(O.,) 0 L,,

rl(0g,0m) ]

r2(0e, 0rn)

+ [ Cll(0,,0=,0e) C21(0£,0rn,_e) C12(0e,0m,0e) 622(Orn,_m) ] [ _rn0e ] "_-

[ De(de) r2(Oe, 0_)

= [ o.,(Oe, gdOe, 0.,) O.,)]+[ Nk'(O_'O'') -kl(Oe, Ore) ]

= [o] I u (3.16)

(3.17)

Subcase : Symmetric motor inertia

If the inertia of the motors is symmetric about their axis of rotation, (3.16,3.17) become

+

[ Dm(O_) De(Oe) ] +

[ '/'l(Oe, Om) ] r2(Oe, Ore)

u (3.18)

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[ ][ r_(Oe, 0.,) gdOe) +

r2(Oe, 0.,) = o -kx(Oe, O_)

This model has the same structure as used by Spong [27] and many other researchers.

(3.19)

3.3 Notation and useful bounds

We define the position error and velocity error as

Ae = e - ed, ,.x_= _ - 0d

and so on for higher derivatives.

= - .z "...... =:.7.: 2_ - . .

Also, O(i) denotes the i*h derivative of O, and a function f E C t" if it is continuous and differentiable k

times. Furthermore, the i tn row of a matrix or the ith element of a vector will be noted by the subscript

", i" (without the comma for a matrix or vector without subscript), and a particular element of a matrix

will be denoted by its coordinates in parenthesis as a subscript.

The following notation for various bounds will be used later in this paper:

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3'_= supII_O(t)ll

¢_>o

_M = sup IIM(0)II

0

7n = sup IlVo_(O)ll

0

Otp = Gmi n {R + BI(pB T }

t>o

_ 0M(0) 7D = sup 7

uvi

7Do= IIDoll

Ted = sup [ A0(t)

t_>o

7p = R+ BKpBT[

%, = [Do + BI(_BT H

where Ifp, IQ, E R.nx'_ are controller gains.

The following results will also be used in the paper.

Due to the positive definitiveness of the mass matrix M in (3.1), and of Mll and -M22, we can always

solve for 0e and 0,_ from (3.10). In (3.10), dropping the dependence on the state in the equation, define

P2 = C21 C22 t}m + Dm r2

Then, we have, for the general case,

Ot -- [2_'11- _/-/12"_//'2"21/_/I5] -1 [t_//12M221 (P2 - t/') -pl]

o..=

(3.20)

(3.21)

Also, the following relations hold:

ca(0, 0,0d) _- c.(o,o)o_- {M_(O,O)O, +{M.(O,O,)O

=

2

1 I

= _CD(O,O)Od+ _cv(o, he)d

(3.22)

(3.23)

(3.24)

Proof : Equation (3.23): direct substitution of (3.8) in (3.22).

Equation (3.24): add and subtract 1⁄4iT(0, 0)t}d to (3.22), and use (3.7) and (3.8).

L

m

J & AOT [CD(O, O)AO + -{1Mo(O,O)Od - {MD(O, Od)O- -_1 2fl(0,0)A0] = 0

Proof : By direct substitution,

r

1MT(0, t})A0 ÷ 1MD(O,O)Od. _1MD(O,0_)0- -_t Mo(O,M)O] J = AoT I.'MD(O' O)AO- -_

(3.25)

11
Addandsubtract1⁄2MD(O,0)0 to this equation and use the linearity property of MD in its second argument

to obtain

- 0

due to the skew-symmetry of the term in brackets.

. =

I

I

i

!

i

IIM(Od) - M(0)II _<7D [I/XOll (3.26)

Proof : "I'D corresponds to the supremum of the norm of the gradient of M, leading directly to this

identity. •

Proof : Add and subtract 1⁄2MD(O, 0)0 (inside the norm operation) and use (3.6) to write

J_ = 1-[MD(O,O)_- MT(o,o)Ao - MD(O, AO)Ol

2

-< 7 7 MT(o'o)[ A0 +7

and use (3.5) to write

which concludes the proof.

Jn

3

= _TD 3 _oq- o_111_ o

- 3 A0 _ 3_-I°_1_°11

IIca<0,0,0_) _3 10_11 =

Proof : Use (3.23) to write

[ca(o,o,od)[

3

• _1°_1 _°1

3

which leads to the conclusion by using 11011< 0_I÷I_°tl •

(3.28)

I_I"VID(O,O)Od-- I MD(O, Od)OI < TD Odl l A_

(3.29)

12

i

m

m

J

i

|

[]

[]

Ill

I

I

=

I

I

il

B

i

I

!

I

I
= :

i

F

Proof : Add and subtract 1⁄211,ID(O,d4)Od (inside the norm operation) to write

illMD(O,d)dd - 1A/ID(O, dd)O -- 121,¢D(8, Ad)dd - 2MD(8, dd)Adll

< 3`Dld_ ,,0

which ends the proof.

F_1

i

I

_1_(o_,o_)o___(o,o,o_)i1_ __._io_1',=o,+-_, o_I=ol (_0)

Proof : Use (3.22) to write

Add and subtract CD(O, Oe)dd and 1_MD(O, T de)de (inside the norm operation) to obtain

Jm = IlCD(Od, dd)Od - CD(O, dd) dd --CD(O'AO) rid+ 1MD(O'AO)dd--1AZlD(8'dd)AO I

1MD(O,/_d)dd 1 MD(O, Od)Ad ]

1

3 3

Where we have used the definition of MD (3.5), and in particu]ar, the supremum of the bound of the

gradient of MD with respect to 0 to obtain the bound in "[Dd. •

For C represented using the Christoffel's symbols, the relations listed below hold.

There exists a unique and finite real constant 76' that satisfies, for any 0 and any finite w,

IlCc(O, w)ll < 3`cI1'_11 (3.31)

Proof : The evaluation of this bound, and in particular of a tight bound 3'6" is not straightforward and

is not provided here. •

There exists some constant 7cd so that

Proof : This bound is a function of the supremum of the norm of the gradient of Cc but is difficult to

find analytically so that no proof is provided. •

Also, there exists some constant 7c2 so that

Proof : We may write

I1_(o,o>.o- ,_(o,o>,,oll ___(o,o),',o I+1_,(o,o/_,o I

-<",cll_°l 1°I+-I_°11I°11

__ 13
where we have used (3.4) and (3.31). We obtain the desired conclusion by choosing 7c_ = 7c + 7D, which

gives a loose evaluation of the bound. •

(3.34)

Proof : We may write

Cc(Od, &) - Cc(O, o) = co(ed,&)- Cc(e,&)- Cc(e,a ) H

< ":CdII_OII Od + 7C I:'OI :

where (3.32) has been used in the last step.

Finally,

IV(O)- _(Od)ll_<7RII:--XOll (3.35)

Proof : 7R corresponds to the supremum of the norm of the gradient of _, leading directly to this

identity. •

4 Controller design approach

The synthesis of the material presented in Section 2 leads to a control design approach based on passivity

for a large class of systems. Assuming that we can form an error system and render the error system

passive by static feedback, we can then L2-stabilize the error system by using any finite gain strictly

passive controller. Then, if furthermore the error system with static feedback is at least weakly zero-state

detectable, then internal asymptotic stability is also guaranteed.

Flexible joint manipulators are members of the class of systems to which such an approach can be

applied since such systems are feedback equivalent to passive systems for a proper choice of outputs, and

in particular for any arm configuration if the motor velocities are used as outputs as shown in Appendix

B.

g

m

z

E

m

I

t

[]

I

i

m

m

u

mm

i

I

Problem statement Consider the dynamical equation of the system (3.1,3.2) rewritten here for completeness:

M(O)O + C(O, 0)0 + D(O) + r(O) = Bu

_(0) g(o) + k(o)

T

Find u so that the output of interest y = h(Oe) -- Yd asymptotically.

We now present a design procedure that involves essentially two steps defining the basic structure of

the controller, i.e. (i) define a model-based feedforward in order to form an error system; (ii) add a

model-independent stabilizing feedback to the feedforward.

- f = _ ±

1. Feedforward Design: Decompose the control input as

u = uo + ull

B

=__

mm

U

m

E

14 W
= =

t

z :

L

m

w

Y

Error

System

Yv

Yp

Figure 1: Structure of the proposed controller

o

where u_, is the feedforward input and uo the stabilizing input. The feedforward is chosen such that

the output of interest y tracks the desired trajectory under matched initial conditions. The choice

of feedforward must also allow to form an error system that is passive or can be rendered passive

between a particular input/output pair (u_, y.) via static feedback from some output yp (Fig. 1).

The results presented in [5] can be used to guide the choice of the output y, (Appendix B).

Feedback Stabilization: Find a static feedback (inner loop Cp) to obtain passivity and zero-state

detectability for the pair (ul, y.). Then choose a finite gain strictly passive feedback (outer loop C,)

between the passive input/output pair. By the Passivity Theorem [9], this guarantees L2-stability

of the system from u2 to y_,. Use an energy based Lyapunov analysis to show that output yv _ 0

asymptotically. From zero-state detectability, the zero error state is also asymptotically stable, and

the output of interest y ---*Yd asymptotically.

NOTE : Three outputs are defined in the design procedure: In the case of flexible joint robots, the

output of interest y is generally defined in terms of _e, while _,_ (or A#,,_) has been used as the passive

output Yv, and _m (or A6m) has been used as yp in [16, 24] to render the system zero-state detectable

from ul to y_.

NOTE : Weak zero-state detectability is also sufficient to guarantee asymptotic stability given y, ---. 0

asymptotically.

This approach has interesting features, namely :

• C,, can be any BIBO strictly passive feedback and can be tuned for performance enhancement.

Note that BIBO stability is implied by strict passivity for finite dimensional linear time invariant

systems.

• Only yp and y,_ are needed for stabilization, i.e. only motor position and velocity for flexible joint

robots.

• Error in the feedforward does not cause instability, i.e. small errors in the feedforward only lead to

a weaker form of stability (see Section 8).

• Applicable to both position set point stabilization [16] an d tracking control.

The design approach offers a certain degree of flexibility in the sense that certain choices must be made.

In particular, different forms of feedforward can be used yielding different characteristics regarding their

15
solution(solubility,requiredmodelinformation,requiredsignalmeasurements,comple_ty) (Section5),

andthetype of stability andtrackingaccuracythat areobtained(Section8). Anotherchoicethat hasto

bemadeis whichcontrollerin the classof strictly passivecontrollersshouldbeusedin orderto obtain

the desiredperformance.Among this classof controllers,wewill usethe classicalPD (ProportionalDerivative)controllerto carryout the designin Section6.

In the followingsection,wepresentvariouspossibleforms of feedforwardfor the trajectory tracking

problemandaddressthe issueof solvingthefeedforward.Wethencarryout the controllerdesignin Section 6 for particularchoicesof feedforwardunderthe assumptionthat the conditionof (weak)zero-state

detectabilityis met,assumptionthat is verifiedin Section7. The design approach guarantees asymptotic

stability only if all the design conditions are met, which is strongly dependent on the feedforward signal

that is used. A Lyapunov stability analysis is carried out in Section 8 to analyze the stability of different

forms of feedforward, in particular approximations of the feedforward based on the inverse plant.

5 Feedforward: selection and solution

In this section, we present a series of possible forms of feedforward for the problem of trajectory tracking

and address the issue of solving for the feedforward. In particular, conditions for solubility including

required measurement and modeling information are stated.

5.1 Selection of feedforward

Assume that, given ya = h(0ed), we can solve for 0d. There are many possible feedforward compensation

based on the inverse dynamics of the system (FF1 below) and its approximations that can be used, e.g.

FF1. Buf

FF2. Bur

FF3. Bur

FF4. Buff

FF5. Bur

FF6. Bur

FFT. Bur

FFS. Buff

= M(Od)Od + C(Od, Oa)Od + D(Od) + r(Od)

= M(Od)Od + C(Od, Od)Od + D(Od) + _(0) + ROd

= M(O)Od + C(O, O)Od + D(O,t) + r(Od)

= M(O)Od + C(O,O)Oa + D(Od) + i'(O) + ROd

= M(O)Od + CD(O,O)Od - 1⁄2MD(0,0)0d + 1⁄2MY(0, _Jd)0 + D(0d) + r(0d)

= M(O)Od + CD(O, O)Od

= r(Od)

= + ROd

Note that feedforward FF7 is the same as used for the set point control, e.g. [16, 31].

There are multiple considerations in the choice of feedforward, namely, the measurements and model

information required to solve for the feedforward and for its implementation, the comple_ty of the

solution, and the performance it allows to obtain regarding stability and tracking. We now analyze the

solution of the different forms of feedforward and will analyze their performance in Section 8.

The solution of the feedforward is a stable inversion problem. We will use a procedure for the solution

that involves essentially three steps defined as follows

1. From the last m equations of the feedforward equation (FF1-FF8), define what signals are required

to compute the feedforward.

I

II

I

m

I

i

|

i

m

II

i

R

l

U

m

U

U

U

w

I

g

I

m

16 m

U
m

. Given the desired output (Bed and its higher derivatives, i.e. link reference trajectory provided by

the user) and the signals available by measurement, determine the desired internal state (8,_ d and

its higher derivatives as required by the feedforward, i.e. desired motor reference trajectory) by

using the first m equations of the feedforward, and possibly additional information such as the

original system equation. The result that we are seeking at this step is a causal and bounded

desired internal state yielding a bounded feedforward signal provided that the desired output state

is also bounded, i.e. it should be a stable and causal solution.

3. Evaluate the feedforward.

Proposition 5.1 Consider the properties of the system (Section 3.1) and assume that D(x) and r(y)

are bounded functions of their argument. Then, if the desired trajectory (Od and its higher derivatives) is

bounded, u_ is bounded in

=

r ;

u

i

w

• FF1 andFFT,

• FF2 and FF8 if the following assumption is satisfied

Assumption 5.1 II/XOllis uniformly bounded in time by a finite constant 7_.

• FF3 and FF5 if the following assumption is satisfied

Assumption 5.2 A8 [ is uniformly bounded in time by a finite constant %d.

• FF4 and FF6 if both Assumption 5.1 and 5.2 are satisfied.

Proof : Consider FF4 with C represented using the MD-notation. Premultiply the equation of FF4 by

B T and use (3.9) to obtain

u_ = B T [M(O)Sd + CD(O,8)Sd + D(Od) + r(O) - RAO]

,..I, _< 0 1+

_<II TI[.M + 0 110 1+IID< >I ÷N.<A0+0 >Jl ÷'l llim.,0tl]

which leads to the conclusionof the proposition since IB_land IIRIIare also bounded. The same procedure isused forthe other forms of feedforwardand forthe C matrix representedusing the Christoffel's

I !

symbols.

The following assumptions will be particularly useful for the solution of the feedforward:

Assumption 5.3 rx is continuous and differentiable, and the gradient of rx(Se, 8m) with respect to 8m,

Vo,,ri(Oed, Ore), is invertible in some open set in Or,.

Assumption 5.4 D(8) is continuously differentiable as many times as this function needs to be differentiated to solve for the feedforward.

We will also assume that the reference output trajectory is bounded.

17
5.2 Solution of FF1

ExpandFF1 to obtain, for thegeneralmodel,

o = Mn(O , + M12(O d,O,,, )Omd+ Cn(Oe , +

+ D (Otd)+ (5.1)

uz = + + +

C22(0md, 0me)0me + D,,,(0,,,_) + r2(0ed, 0me) (5.2)

To obtain a closed loop form for the feedforward control input u# from (5.2) for (0ed, 0ed, 0_e) given , we

must first define

=

The problem of solving for (Omd, Omd, O_d) resumes to solving (5.1) for these variables given the input #ee

and 01d, 0_d" This problem may have multiple solutions as in the case where M12 is nonsingular. In that

particular case, the user is free of choosing the initial values for Ome, 0,,_. An important characteristic

that is desired for the solution is its stability in the sense that the desired motor state should stabilize

in order to obtain internal stability not only of the error state, but also of the actual state. This allows,

among others, to reduce the stress on the equipment.

Zero-state detectability or weakly zero-state detectability properties of (5.1), with y = (Oed,Oed,Oed),

and x = (.Omd,Ome,Omd) in Definitions 2.8 and 2.9, allows to characterize partially the solution for

(0-*d, 0,-,*d,0,,*d) either locally or globally.

Fact 5.1 Zero-state detectability of (5.1) constitutes a necessary condition for uniqueness of the solution

Ior

Fact 5.2 Weak zero-state detectability of (5.1) constitutes a necessary condition for the solutions for

(O,,_d,O,,_d,O,',,d) to be asymptotically stable.

A more global condition is obtained if we require that 0ee constant implies 0me = 0c (zero-state detectability of any shifted state), or implies (0,,_, Ome, O"d) -" (0_, 0, 0) (weak zero-state detectability of

any shifted state), where 0¢ E R _ is constant and uniquely defined for each 0e_.

Example 5.1 One case for which (5.1) is zero-state detectable and FF1 has a unique solution is the

following. First, assume that the inertia of the motors is symmetric about their axis of rotatlon, and that

Mr2 is strictly upper triangular (Section 3.2.3). Also assume that the i th element of r_(0e, 0m) depends

only on (0_,i, Ore,i). Under these assumptions, expand FF1 to obtain

0 = M_l(0_a)0 Q -t- M_2(O_e)Orn,,-b c1AI(OQ, OQ)OQ -t- cB(oee, O,nd)Oee H- cA2(oQ, Oea)Orne +

De(0e_) + r_(0ea, Om_) (5.3)

ug = MT(oea)gee + :_'[==g,_a+ C_4_(0ed,de_)de_ + Dm(0md)+ "=(0e,,0,,,e) (.5.4)

Also suppose {hat Assumption 5.3 holds. This implies that, by the Implicit Function Theorem [3], there

exists a locally unique solution 0,ha to (5.a) for any given _J,,_e, _/,,_a, and 0ca and its higher derivatives.

However, _J=a and _/,,,e are not known a priori.

In order to solve for 0m_ and its derivatives, we take advantage of the fact that M_2 and C A are strictly

upper triangular and that row i of C_ does not depend on any O,,,,j for j _< i and i = 1,...,m. This

allows to solve iteratively for the elements of 0m_ from its m _h element to the first.

Given 0ca and its higher derivatives, use the last row of (5.3) to solve algebraically for 0,,_,_. Then

suppose that Assumption 5.4 holds and evaluate _Jm_,,, and Oma,m by taking the time derivative of Oma,m.

Now, given 0m_,,_ and 0=e,=, use the second last row of (5.3) to solve algebraically for 0_,,___, and

continue the process down to Omaa. Note that the desired trajectory has to be very smooth since the

18

i

m

m

mm

!

em

g

R

m

i

[]

==

U

mmm

mm

U

u

m

l
T.

u

ILJ

w

|2

L_

u

z_

L

m

02(I+1) i : 1, ra, and that the characteristic solution of the last term of the iteration, i.e. 0rod,l, requires td,i ,

of the equivalent spring of joint i be differentiable 2i times in both its arguments for i = 1, m.

Noting that all the coefficients of the equations use to solve for the feedforward are bounded, we conclude

that the desired internal state is bounded given a bounded desired output trajectory.

We then obtain the feedforward input by substituting the variables in (5.4).

Hence, the solution of u/_ for this case requires invertibility (at least local) of rx and of V0,, rl along

with knowledge of the full model, a very smooth input trajectory and differentiability to a high order of

rx. However, no measurements are required. []

5.3 Solution of FF2

Expand FF2 to obtain, for the general model,

0 -- Mll(Oee, Oma)Oga + M12(0Q, Orna)Omd + Cll(Oga, Omd, Ogd, Oma)OQ +

C12(Oed, 0rod, 0ee, 0-_e)t}_e + De(tied) + _x(0e, 0_) + RllOee + R120,,,e (5.5)

= Mr(O ., Om.)Oe.+ + +

Cm(Oma, Oma)Omn + Dm(Omn) + _(Oe, Ore) + R2_Oen + R220ma (5.6)

To obtain a closed loop form for the feedforward control input u# from (5.6) for (0_d, t}_a, 0ed) given, we

must first define (0,_d, 0,_d, 0rod).

The problem of solving for (0ran, t)md, 0_d) is very similar to the one encountered with FF1 except that

(5.5) depends on the actual state (0e, 0_). Hence, the feedforward and the control input are intertwined.

This renders the task of characterizing the solutions more difficult: we must know the controller in order

to ascertain stability of the feedforward input. However, we may assume that the closed loop system is

stable and that the error signals are bounded in order to pursue the analysis.

Example 5.2 Consider Example 5.1 with the additional assumption that RI_ is chosen nonsingular and

upper triangular (or diagonal). Expanding FF2, we obtain

0 = Mxl(OQ)Og e + M12(OQ)Omd + cA(oQ, OQ)OQ + cB(oQ, Orna)Oee + C:2(Oea, Oed)Ornn +

D_(0ga) + fl(0e, 0m) + RllOed + Ra2Ome (5.7)

u_ = mT(oea)Oea + M220,_ + C_aa(Oea,Oea)Oe_+ D,,_(Om_) + _2(Oe,O,.) + R_xOea + R_O_ (5.8)

In order to solve for 0ma and its derivatives, we take advantage of the structure of M_2, C_, cA and R_

to solve iteratively for the elements of 0_ a fi'om its m th element to the first.

Given 0e_ and its higher derivatives, and assuming that 0e and 0_ are available for measurement, use the

last row of (5.7) to solve algebraically for the m _h element of Omn:

Omn,m = -Rta,(m,m) -_ [M,x,,_(Oen)Oe_ + Ci41,m(OQ,Oea)Oe_+

De,m(Oee) + _'l.m(0e,_, 0re,r,,) + R_x,(_,_)Oe,,_] (5.9)

We solve for 0,_n,_ by taking the first time derivative of (5.9) under the assumption that tie and 0m are

available for measurement and that Assumption 5.4 holds. Now, taking the second time derivative of

(5.9) in order to find _/mn,_, we obtain, for X properly defined:

-1 d2

Oma,r a -" -R12,(m,m) -'_ [J]lll,m(Oga)O_a + Ci_,m(0ea,t)en)t}en+

-- _ , . ,uf. d .t_fd _Vt,m, . , ,rn

-1

r O: 0 0 Vo .... f_ ,

- R_2,(,n,ml [V0Cm - ""

l,m( .,rn, re,m) t',m + ,m(O'_m'Orn,m.)Orn,m]

= 19
Since t_t and 0,_ are usually not available for measurement, we want to remove the dependence on these

variables. For this, we use the dynamical equations of the system (3.20, 3.21). ffowever, due to the

gyroscopic coup]_ings, 0m,m may depend directly on all the inputs, i.e. in general we must know all the

feedforward inputs in order to solve for this signal. We conclude that, in general, this approach does not

lead to a practical closed loop form for the solution in this case. t2

NOTE : Under the additional assumption that Mx2 is constant in the previous example, the same

problems are encountered in the solution of the feedforward.

Example 5,3 Continue the previous example(Example 5.2) with the assumption that there is no gyroscopic coupling, i.e. Ml2 = 0 (Section 3.2.4). Expanding FF2, we obtain

0 - Mll(0e_)0t_ 4- C::(0ea,t_td)0t_ 4- De(t_) 4- ::(0e,0,n) 4- R:18e d 4- R120m_ (5.10)

uz = M220,_ 4" D,_(0m_) + f2(Oe, Om) -4-R_lOed 4- R228,_d (5.11)

Given 0_d and its higher derivatives, that R12 is nonsingular but Otherwise arbitrary, and assuming tllat

0_ and 0m are available for measurement, we have, from (5.10),

0,,_d = -R12 -1 [Ml,(0ed)0ea + C,l(8td,O_,)Old + De(Oct) + _l(8e, Sm)+ Rl18/,] (.5.12)

and 0rod is bounded if Assumption 5.1 is satisfied.

We solve for t_,nd by taking the first time derivative of (5.12) under the assumption that 0e and 0m are

available for measurement and that Assumption 5.4 holds. Now, taking the second time derivative of

(5.12) in order to find @,,_, we obtain, for X properly defined:

d2

0,nd = -R12-'_-_ [M,,(0ed)0ed + C,l(Oea,Oed)Oed 4- De(Oea) 4- fl(Og,O,_) 4- RHOea]

+ l (5.13)

We now use the dynamical equation of the system to write _e and 0m as a function of know signals. From

(3.20, 3.21),

= MH(0e)-' [C,l(Oe, Oe)Oe + De(Oe) + r,(0e,0m)] (5.14)

= -M_' [D_(0_) + _(0,, 0,,)] + M_'u (5._5)

Substitute (5.14,5.15) in (5.13) to obtain, for .¥2 properly defined,

0,,_s = X2 (0e_, 0e_, 0e_, 0(3)t,t' 0(4}_, Oe,#e,O,_,Om)- R_-'Vo,_(Oe, O,n)Mfz_u (5.16)

which is bounded if AssumPt!0ns 5.1=and 5.2 are satisfied and if £x0 remains bounded.

Use (5.11) and (5.16) to write

= + +

where the term (u - u_) represents the contribution of the stabilizing controller, and under the assumption that [I+R_:-_Wo,,,_(Oe,O_)] is invertible for all (0e,0m), which is always possible by a proper

choice of R_2.

2O

U

i

m

I

i

|

J

I

m

J

m

m

u

m

I

u

=

J

m

m

m

R
=

= =

Hence, the solution of u_i, for this case requires invertibility Of R12 and of [I + Ri2 -1V0,. fi (Or, 0m)] along

with knowledge of the full model, measurement of the full state, differentiabihty of "l twice in both its

arguments, and knowledge of the stabilizing controller signal. This last requirement also means that the

feedforward and the feedback are intertwined. []

w

z .

u

7

=

w

E}

2

E:

5.4 Solution of FF3

Expand FF3 to obtain, for the general model,

0 = Mll(ee, e..)$_ + M12(ee,e..)_m. + C.(ee, a..,0e,0m)0,, + C_2(e,,e..,0_,&.)0.._ +

Dt(0td) + rl(0t,, 6m_) (5.17)

ug --- MT($e, Sm)Oee + M22(Om)gme + C2i(Oe, Sin, Oe,O,n)Oed + C22(0m, Om)Ome +

Dm(0_) + _:(0_, 0m_) (5.is)

To obtain a closed loop form for the feedforward control input u//from (5.18) for (0_,, 0_, 0l,) given , we

must first define (0m_, 0,,_d, 0m,).

The problem of solving for (0m d, 0m,, 0m_) is very similar to the one encountered with FF1 except that

(5.17) depends on the actual state (0t, Se,6m,0,_). Hence, the feedforward and the control input are

intertwined. This renders the task of characterizing the solutions more difficult as noted in the discussion

on the solution of FF2 (Section 5.3).

If we try to find the closed loop solution under the same assumptions as in Example 5.2, in particular

M12 strictly upper triangular, we encounter the same problems as with FF2. We will thus consider the

system in Example 5.3 to demonstrate the solution of this feedforward.

Example 5.4 Assume that the inertia of the motors is symmetric about their axis of rotation, and that

Mi2 = 0 (Section 3.2.4). Also assume that the i th element of ri(Se,0m) depends only on (0t,i,0m.i).

Under these assumptions, expand FF3 to obtain

0 = -Mil(Oe)0ed + Cu(_e,0e)0ee + De(0ed) + rl(O_,,8_e) (5.19)

_z = z_h_ii.._+ D..(0_) + _(0_, 0...) (5.20)

Also suppose that Assumption 5.3 holds. This implies that, by the Implicit Function Theorem [3], there

exists a locally unique solution 0,,_ to (5.19) for any given 0e, 0_, 0e_ and 0e,.

To solve for 0,_ and 0m,, assume that r_ is differentiable twice in both its arguments and that Assumption

5.4 holds. Take the first time derivative of (5.19) to obtain:

0 = £I_l(Oe, Oe)Oe, + Mll(Oe, _e_"e. i_ ,a(3) + d'_i(0e,he,/ie)0e.+ Cl_(Oe,Oe,Oe)Oe.+

(5.2i)

We then solve for 0e by using the system equations in order to remove dependence of the feedforward on

this variable (see (3.20)):

O_ "- --1_/11(0_) -1 [Cll(O_, Ot)Og nL Dt(Oe) + r,(Oe, 0.,)] (5.22)

By substituting (5.22) into (5.21), we may write

(5.23)

such that we may solve for _),_ by Assumptions 5.3 and 5.4 provided that 0m, has been previously

evaluated and that 0e, 0e and 0m are available by measurement.

=: 21
We.proceedsimilarlyto evaluate_'mabytakingthefirst thnederivativeof(5.23),removingthedependence

on8_ by use of (5.22), and by assuming that 0,_ d has been previously evaluated and that _,,_ is also available

by measurement. We note that the fourth time derivative of Bed is required.

The internal state remains bounded if Assumptions 5.1 and 5.2 are satisfied.

We then obtain the feedforward input by substituting the variables into (5.20).

Hence, solution of ug requires invertibility (at least local) of rl, that rl is twice differentiable in both its

arguments, along with knowledge of the full model and full state measurement. []

5.5 Solution of FF4

Expand FF4 to obtain, for the general model,

0 = Mn(0e, O,_)/_td + M12(0_,0m)_',,_d + Cll(Oe,6,n,_e,O,n)Sed + C12(Ot, O,n,_t,_,n)_,,_d +

De(8td) + e,(St, O,_) + RllSea + R120,_d (5.24)

uz = M (Oe, Om) a+ M22(&) m + + +

Dm(_m_) + e2(_, 8m) + R210ed + R_20._a (5.25)

To obtain a closed loop form for the feedforward control input u/_ from (5.25) for (Sea, 0ca, _ea) given, we

must first define (0rod, _,na, 0rod)"

The problem of solving for (0,ha, 0'_d, _,nd) is very similar to the one encountered with FF2 except that

(5.5) depends on the actual state (0e, 0_, 9m, _m).

Example 5.5 Consider the system used in Example 5.3. Expand FF4 to obtain

0 = Mll(Sl)_a + Cll(Se,_e)Oea + D_(_ea) + Fl(0e, 0m) + Rll0L_ + R128ma (5.26)

u g = M228,_ + D,_(O,na) + v_(Ot, Om) + R_Oea + R_2Oma (5.27)

Given 0e_ and its higher derivatives, that R_2 is nonsingular but otherwise arbitrary, and assuming that

Or, 8e and 0,n are available for measurement, we have, from (5.26),

Om a = -R12 -1 [MII(Oe)_ta + Cll(Ot,_e)_Q + De(_ta) + rl(0e,0m) + _,lOta] (5.28)

Suppose that Assumption 5.4 is satisfied. We solve for _,_ by taking the first time derivative of (5.28)

under the assumption that _m is available for measurement, and by removing the dependence of this

equation on _t by using the system equations as done in Example 5.4. Now, taking the second time

derivative of (5.28) in order to find _,,a, and substituting _t, 0_3) by functions of measurable state only,

we may write, for X properly defined:

i.e. 0rna is of the same form as in (5.13) so that we pursue the solution as in Example 5.3, and the

same conditions for solubility are obtained as for FF2. Furthermore, the signals remain bounded if

Assumptions 5.1 and 5.2 are satisfied.

5.6 Solution of FF5, FF6

The solution of FF5 and FF6 are respectively very similar to those of FF3 and FF4 such that the

reader is referred to Sections 5.4 and 5.5 for information about their solution.

m

!

m

R

m

I

i

m

m

i

m

M

N

i

m

m

I

mm

m

g

m

22 i
t

m

M

L •

m

±

i

w

7_

m

5.7 Solution of FF7

Expand FF7 to obtain

0 = rx(e_ e,omd) (5.29)

uz = r2(O_,.Om_) (5.30)

To compute the feedforward control input u/_, from (5.30) for a given 0ed, 0rod must first be defined.

Suppose that Assumption 5.3 holds. Then by the Implicit Function Theorem [3], there exists a locally

unique solution 0rod to (5.29). The solution can then be substituted in (5.30) to obtain u£.

Hence, solution of u£ requires invertibility (at least local) of Va,,,rl(Oe, #m) along with knowledge of r,

i.e. gravity load and spring characteristics. No other restrictions are imposed on the system and no

measurements are required.

If higher order derivatives of Stud are required for the controller, we may proceed as follows. In order to

obtain the ith derivative of 0,,_e further assume that rl is differentiable i times in both its arguments, and

that all higher derivatives of 8re up to the i th are available. Take the first time derivative of (5.29) to get

0 = v0, _l(e_,om,)o_ + v0.. _(o_,,o.,,)om_ (5.31)

given that 0,,,_ has been previously evaluated.

Taking the time derivative of (5.31), and given that 8,,_a and /_"_d have been previously solved, 0-_a is

found in the same fashion. Hence, we can solve iteratively for the higher derivatives, and boundedness

of the signals is guaranteed for any bounded desired output trajectory.

Example 5.6 A common case for which we can solve for the feedforward is as follows. Assume that the

inertia of the motors is symmetric about their axis of rotation (Section 3.2.2), and that kl is defined as

follows

(kx)ij(Oe, Ore) = fi(NiOei 0 - Ornl) i =# j i,j = 1,''' ' n (5.32)

where Ni and fi are respectively the gear ratio and the elasticity characteristic of the ith joint. If all

the fi's are monotonically increasing, continuous, differentiable, and their range is R, then kl is globally

invertible.

Equations (5.29, 5.30) may be written as

o = g_(Oe,) + Nk_(O_d,O,,,_)

uz = -kl(Oe,,,O,,,,,)

and a unique solution, 0,na, to (5.33) can be foun_:_orany0td and is given by

= k;

We obtain u# by substituting (5.35) into (5.34) which gives, after simplification:

utf = N-lge(Oed)

(5.33)

(5.34)

(5.35)

o

Note that for the previous example without the motor symmetry assumption, a local result may be

obtained under proper assumptions.

23
5.8 Solution of FF8

Expand FF8 to obtain

0 = _!(Se, 0rn) + Rllt_Q + R128rnd (5.36)

Uff -- _2(_,_rn) -l- R21_ed at- R22t_rnd (5.37)

To compute the feedforward control input u//from (5.37) for a given $ed, we assume that _l and 8,,_ are

available for measurement and the reference position _-_d must be defined.

Assume that R is chosen with R12 nons-ingular] we obtain 0ran=from (5:36)_ : =

...... ore,= -Rl( [e,(e,,o,,,)+ RI 0,,] (5.38)

We obtain u// by substituting (5.38) into (5.37):

U ff -_- r2(0t, 0rn) + R210td -- R22Rx2 -1 [fl(Oe, Om) -t- lrlzzOta]

= (5.39)

and all the signals are bounded if Assumption 5.1 is satisfied.

We see from (5.39) that if R is singular, then Bed does not affect u// directly in certain directions. However,

there seems to be no reasons at this point to restrict R to be nonsingular.

Hence, solution of u// requires invertibility of R12, which is achieved by proper design, exact knowledge of

r, i.e. gravity load and spring characteristics and measurement of motor and link position. Also, solution

of the feedforward imposes no restriction on the structure of the system and on the system parameter

values.

However, the controller may require higher order derivatives of _ma, in particular _,nd to implement a

PD motor loop. Given _td, _-_d is obtained by taking the time derivative of (5.38) assuming that el is

differentiabie in both Oe and/9,n and that measurement of motor and link velocity are available:

= -RI +

where Vz_I(x) is the gradient of _1 with respect to x. Note that the condition of differentiabilit.y of

_1 implies a similar condition on rl, i.e. on the spring characteristic itself. For boundedness of/gm_,

Assumption 5.2 is required.

6 Controller design for tracking

In this section, we carry out the controller design, i.e. error system stabilization, for the error system

formed by using using FF4 and FF6 defined in Section 5.1 in order to demonstrate the application of

the procedure established in Section 4 and to present some stability results and design requirements. We

recall that the procedure allows to ascertain asymptotic stability for the system by using any controller in

the class of Strictly passive and BIBO stable controllers if we can show that yv ---, 0 and if the zero-state

detectability condition is met.

6.1 Stabilization of error system formed by FF4

For FF4 with C represented using Christoffel's symbols (Section 3.1), define

U = uo q- ttff

to obtain the following error equation for the system :

M(8)A[9 + CC(8,_)A_ + D(_) - D(_a) + RAP = Buo (6.1)

24

w

u

ll!

i

m

z

m

il

il

Ill

i

Ill

z

I

[]

N

I

IIII

II

m-w =

I

m

D
" 6.1.1 Static feedback

[J

L_

L_

w

m

w

_u

n

In Appendix B (Section B.2), we show that the Use of the motor position as part of the output may allow

to obtain stable zero dynamics for flexible joint robots, implying at least weak zero-state detectability,

and also to render the system passive if, for example, the motor velocity is also part of the output (see

case 1 of Section B.2). This motivates the following design.

Consider a static feedback of the motor error state (Cp(yp) = I(pBTAO):

uo = ul -- KpBT A8

The closed loop error equation becomes:

M(8)A_ + Co(#, 0)At) + D(_) - D(t_d) + [R + BKpB T] A8 = Bul (6.2)

Proposition 6.1 The pair (ul, BT As) is a passive pair for the error system if the two following assumptions are satisfied:

Assumption 6.1 R and Kp are chosen such that [R + BKpB T] > O.

Assumption 6.2 There is no negative damping in the system.

Proof : In order to show passivity, consider the following energy function based on the total energy of

the error system

Using (6.2,6.3) and the fact that (1⁄2/_3/- C¢) is skew-symmetric, we obtain

¢ = A8 TBul - A8 T [D(_) - D(Sa)] (6.4)

Evaluate the time integral of (6.4) :

v(t) - y(to)

/I A_T Bul dt V(t) - V(to) + ftl A_T [D(t_)- D(_d)] dt

Assumption 6.1 implies that V is positive definite, and Assumption 6.2 implies that

>__0,

(6.5)

(6.6)

such that (6.6) satisfies the condition for passivity (Definition 2.6). •

Note that the position feedback is not required in general to guarantee passivity, e.g. the spring coupling

matrix for manipulators with linear flexible joints may be used to define R which then takes the form

[31]

R = [ -K_N K_N2 -KeI(_ N ]

and is then positive semi-definite (K, is positive definite). However, the position feedback is useful to

guarantee (weak) zero-state detectability (Section 7).

Also note that the system remains passive in the absence of friction, i.e. if D(8) = 0.

25
Moreover,assumption6.2couldberelaxedby employingthefactorization

D(_) = DI(_) + Dot_ (6.7)

and replacing D(_d) in the feedforward by DI(_) + Do'd, Do >_ O, assuming that the feedforward is

solvable.

Then, [D(_)- D(_d)] is replaced by DoA0 in (6.1, 6.2, 6.4, 6.5, 6.6) such that due to the positive

semi-definitiveness of Do, only Assumption 6.1 is required to guarantee passivity.

6.1.2 Strictly passive feedback

Choose

ul = u2 - C_,(BT A_) (6.8)

where Cv is strictly passive and BIBO stable. Then, by the Passivity Theorem (Section 2), the map from

u2 to BTA_ is L2 stable.

The closed loop error system is given by

M(O)AO + Cc(O,O)AO + D(O)- D(Oa) + [R + BIfpB T] AO + BC_(B TAd) = Bu2 (6.9)

and, using the same energy function V as before (6.3),

(6.10)

Proposition 6.2 If u2 = O, we can conclude that (A0, A_) converges to the largest invariant set in

{(AS, A0): sTAB = 0} for (Od, _d) uniformly bounded.

Proof : Recall that the strict passivity of C, means

wTCv(w) dt> -7 2 + r/f llwll2 dt (6.!1)

for any T > to, w G L2,, where q > 0 is a constant, and the constant 7 depends on the initial condition

of the internal state of C.. Using (6.11) and integrating (6.10), we have

_<

dt

From this inequality, we conclude that V(A_, A_) is uniformly bounded. Also, positive definitiveness of

V implies that A8 and A_ are uniformly bounded. By (3.31), the Coriolis and centrifugal force term in

the error equation (6.9) is bounded above in norm under the assumption that 0d is uniformly bounded.

Hence, given the BIBO stability of Cv, we conclude from (6.9) that A0 is uniformly bounded. Uniform

boundedness of 0d must also be assumed in order to guarantee uniform boundedness of the feedforward.

Uniform boundedness of A0 implies continuity of A0, which, along with the fact that BTAs(t) E L2,

lead to the conclusion that BTAO(t) -- 0 asymptotically by Barbalat's lemma [25]. •

Furthermore, assume that 8d E C k, k > 2, and that D E C _'-2 (no Coulomb friction or exact compensation

of the discontinuous terms). Also consider the fact that M(8) and Cc(8, v) are functions of sin and cos,

and are uniformly bounded as well as all their higher time derivatives if v and its higher derivatives

are uniformly bounded. Then, by taking successive time derivatives of (6.9) and from (AS = 8 - 8d),

U

II

!

m

i

|

m

m

J

!

Ill

i

I

Ill

I

26 D

Z
w

F.

m

z

i

L

n

u

m

we find that all higher derivatives of/X_ up to its (k - 1) th derivative are uniformly bounded, and all

higher derivatives of BTA_ up to its (k - 2)th derivative tend to zero asymptotically. This last result

will be useful in the demonstration of zero-state detectability where the convergence to zero of the higher

derivatives of BTAo is needed.

Proposition 6.3 The zero error state of the system with strictly passive BIBO stable feedback C, and

for u2 = 0 is (locally) asymptotically stable under the following assumption

Assumption 6.3 BT A_ is (locally) zero-state detectable in (A0, A_) with respect to the following equation:

M(0)A_/+ Cc(8,_)A_ + D(_) - D(_d) + [R + BKpB T] /x8 = 0

If the detectability is global, then so is the asymptotic stability.

NOTE : conditions for (local) zero-state detectability will be given in Section 7.

Proof : Proposition 6.2 and the BIBO stability of C_ imply that

ul -" Cv( BT _) -- 0

Substitute this result in (6.2) to get that for BTA_ _ 0 asymptotically,

M(0)A0 + Co(0, 0)A0 + D(0) - D(Od) + [R + BKpB T] AO ---*0

If BTA_ is (locally) zero-state detectable in (A_, A_) with respect to this equation, then, (A_, A_) --, 0

and the error state of the system is (locally) asymptotic stable. •

6.2 Stabilization of error system formed by FF6

The design procedure and requirements for the use of FF6 (C is represented using the MD-notation

(Section 3.1)) are the same as for FF4 in Section 6.1. However, given the new error equation with static

position error feedback

M(O)AO + CD(8, O)A_ + 1Mo(0,0)_d - 1MD(_,_d)_ + D(O) - D(_d)+

[R + BI(pBT] _o = But (6.12)

property (3.25) is used to evaluate the time derivative of V (6.3) to be (6.4), and to be (6.10) after the

strictly passive feedback is added.

Also, identity (3.30) is used to bound the Coriolis and centrifugal force term in the new error equation

for Proposition 6.2 to hold.

7 Zero-state detectability

In this section, conditions for zero-state detectability are defined for feedforward FF4 with C represented

using the Christoffel's symbols and for FF6 in which C is represented using the Mo-notation. In essence,

we want to show that in (6.2), (6.12), if the passive output BT_o and the input ul are zero then the

internal state of the system is zero, or tends to zero for weak zero-state detectability (Section 2).

7.1 General result with link damping

For FF4, consider the error equation (6.2), and the scalar function V (6.3) and its derivative V (6.4).

We want to use V to demonstrate that the link state error tends to zero asymptotically. Suppose that

27
Assumption6.1holds.After settingul, and BT_ to zero, we get, for the general model,

==

E

We note that V is positive definite in A_t. In order to have V positive definite in APt with AS_ = 0 at

its minimum, choose R such that RI2 = -R21T (recall that Rll > 0 to satisfy Assumption 6.i)i We also

have

= -Ae T [D(_)- D(0d)]

Hence, if there is positive link damping, V is negative semi-definite and A0e --, 0. Substituting this

result along with the assumptions on Ul and BTAO in (6.2), we obtain

[R+BI S] =0

which leads to the conclusion that A8 --, 0 by Assumption 6.1, implying that the internal state converges

to zero.

Hence, for any arm configuration and in presence of positive damping at the links, the system is weakly

zero-state detectable (globally) if Assumption 6.1 is satisfied and if we choose R such that R12 = -R21T.

The same conclusions are reached for FF6 by using the same procedure.

E

u

|

|

m

i

7.2 Results without link damping

In order to assess (weak) zero-state detectability, or zero-state detectability, in the absence of link

damping, we use a different approach than in Section 7.1. This will allow us to obtain a general conclusion

along with some positive results for some general cases.

NOTE : As seen in Section 7.1, (ul, BTA_, AOt) = (0,0,0) implies that APe is zero, and hence the

internal state is also zero.

Consider the error equation of the system (feedforward FF4) with static feedback only (6.2). For the

general model (3.10), after setting Ul = 0, and AS,,_ and its higher derivatives to zero, (6.2) simplifies to

Mll(Oe, + + Dd e) - Dd ed) + th Aee + R 2Aa,, = o (7.1)

MT(Ot, O,,)A_t + C21(Oe, D_,_m,_m)A_e + R21Age + R22A_,_ + KvAS,_ = 0 (7.2)

Then take the time derivative of (7.2) to get, at A_m = 0,

MT(0,,#m)A0_ 3) + _:/T(8_, #e, 6,,, #,,)A#e + C_l(#e, _e, #,,,, #,,,)A#e

= 0 (7.3)

For FF6 we may write similar equations.

RESULT : H (7.3) is asymptotically stable (A#e --, 0) then the system is at least weakly zero-state

detectable and a similar conclusion is obtained for the equivalent equation obtained with FF6.

We now consider three general cases for which we seek conclusions on zero-state detectability.

Case 1: MI: = 0

For M_: = 0 and under the additional assumption that the motors are symmetric or that M_ is independent of the motor position, (7.3) becomes

= o (7.4)

28

I

z

[]

m

B

m

ml

I

l

i

R

N

!R
i

u

m

m

u

i

such that, for R2x arbitrary but nonsingular, At_¢ and its higher derivatives equal zero and, as noted

previously the internal state is also zero, i.e. we obtain global zero-state detectability.

NOTE : If M1x depends on the motor position, then we may still be able to conclude zero-state

detectability depending on the structure of C21.

The same procedure may be used for FF6 and leads to similar conclusions.

Case 2:M12 is singular but non-zero

We will particularly consider the case when the motors are symmetric and M12 is strictly upper triangular

as an example (Section 3.2.3).

For FF4, (7.3) becomes

MT(OI)AO_ 3) + __IT(Oe, Oe)AOe + C21(0t,0e)A_/t + C2,(0e,0e,0e)At_e + R21At_e = 0 (7.5)

where M T, C21 and their time derivatives are strictly lower triangular (Appendix A). Then, choosing

R21 to be lower triangular (or diagonal) and nonsingular, it follows from (7.5) that A0_ = 0 (recursively

solve for A0¢,, i = 1,..-,m), also implying A0e = 0.

As seen before, A0 = A0 = 0 implies that the internal state is zero, and the system is globally zero-state

detectable.

However, without the symmetric motor inertia assumption, C21 may not be strictly lower triangular such

that zero-state detectability may not be shown in the same fashion. However, depending on the new

structure of C12, we may possibly show weak zero-state detectability.

For FF6, the derivative of the motor equation with (ul, BTAo, A0e) = (0, 0, 0) is

1

MT(ot)AO_ 3) + #IT(Ot, be)AOe + C2_(Ot, Oe)/XOt + ¢2x(Oe,Oe, Ot)/XOt - _MD,2_(Oe, Oe)AOt

where M T, C2x, MD,n and their time derivatives are strictly lower triangular (Appendix A). Also, row

i of Mm,71(Oe, AOe) depends only on At_e,j, j < i for i = 1,.--,m. Then, choosing R21 to be lower

triangular (or diagonal) and nonsingular, it follows from (7.6) that A0e = 0 (recursively solve for At_e,,

i = 1,2, ...m), also implying A0t = 0.

As seen before, A0 = A0 = 0 implies that the internal state is zero, and the system is globally zero-state

detectable.

Case 3:M12 is nonsingular

At this point, we have no results on the zero-state detectability of the system if M12 is nonsingular.

L

r_

m

u

8 Stability analysis under feedforward approximation

The design approach presented in Sections 4 and 6 allows to obtain a complete class of controller guaranteeing uniform asymptotic stability for systems meeting all the design conditions. Such a case for

flexible joint manipulator is when motors are symmetric and there is no gyroscopic coupling guaranteeing solubility of FF4 and of FF6, and zero-state detectability under some additional design assumptions

(Sections 5, 6 and 7). But if for some feedforward we cannot verify (weak) zero-state detectability, we

cannot draw any conclusions about the system stability at this point. This may append in particular if

an approximation of the exact inverse system based feedforward'is used.

If for some canonical feedforward all conditions are met except possibly the solvability condition (no

causal and bounded solution), then we can draw some information from the stability properties of the

29
systemunderthe canonicalfeedforwardto analyzestability underotherformsof feedforward.To doso,

weusetheidentity that uniformasymptoticstabilityimplieslocalexponentialstability. Wethenconsider

thevariationsin thefeedforwardasperturbationsandfind theboundsontheseperturbationsallowingto

maintainstability for the feedforwardunderanalysis,i.e. weperforma robustnessanalysiswith respect

to the variationof the feedforward(see[36]for the applicationof this methodto rigid robots).

Wewill usethis approachto analyzethe stability of thedifferentfeedforwardformsby usingFF4 and

FF6 ascanonicalfeedforwardcompensations.However,wefirst introducethe formalprocedure.

8.1 Procedure

m

m

II

i

Assume that with some canonical feedforward the zero error equilibrium point is locally exponentially

stable. Then, locally to (A$, A0) = (0, 0), there exists a scalar function V1, V1 = 0 for (A$,/xt_) = (0, 0),

and constants (or,/3,3' > 0) such that

v, > _ Uxll2 (8.1)

f', _<--r II_tl2 (8.2)

[Iv_v_II_<_ I1_11 (8.3)

wherex= [AO T A_ T ].

Proof : To see how these conditions lead to exponential stability, note that (8.3) along with VI = 0 for

(AS, A0) -- (0, 0) imply that for some constant _: > 0,

71 _<:,<llxll2 (8.4)

where we may choose a = _ write

vl = f/ v_v,(o d_ <

Combine this result with (8.2) to obtain

_ f0 II_ll

where a = 2. Now write (8.5) as

Integrate this last equation to get

IW_Vx(_)lld5 <__ Ilzllz

f'_ < -,,v_ (8.5)

d (e"(_-t°)V1) e-_'(t-t°) < 0

dt

<_ [V,(to,AO(to), AO(to))]

i.e. local exponential stability with rate of convergence larger or equal to a is obtained.

Write the different forms of feedforward, u/_, in terms of the canonical feedforward, ulI,, and of a variation

term_ e_ as

Buly = Bull ¢ + e

Now consider a scalar function V2 = V1. The time derivative of V2 along the solution of the system with

the various forms of feedforward is

= + r (8.6)

i

m

[]

m

i

i z

-- !

i

i

i

U

i

[]

m

i

30 m

[]
=!

w

z

m

i

n

= =

i

w

u

w

where M(v) is the multiplier of A8 in the error equation of the system with the canonical feedforward,

e.g. M(0) in (6.9) for feedforward FF4. Write

<

<

Now use (8.2,8.3) and note that IIv_ev_ll

v__<

Vt + [[VA_V1] r M-'(v)e

¢, + ---1llv,,ev, IIIlell

(XM

___IIV_Wtll_<_ I1_11,to write

-7 llzll_+ CIlzllIlell

where _ = ..0_. Then, if the norm of the

_M

described below, we can assert stability of

(8.7)

(8.8)

variation of the feedforward satisfies one of the two bounds

the system under the new feedforward.

(_a,se 1 : Assume that for some constant X > 0,

I1_11< x 11_11 (8.9)

Then, using (8.8), we obtain

V2<- (7 - CX)Ilzll=

Hence, exponential stability with guaranteed rate of convergence al > 0 is obtained if the variation of

the feedforward is small enough in the sense that

x < 2 (8.10)

C

and using (8.4), al is given by

0.x_ _ - Cx (8.11)

/.¢,

Case 2 : Assume that for some constants (X,P) > 0,

I1_11< x 11=11+ p (8.12)

Then, using (8.8), we obtain

_=_<- (7 - r.:x)I1=11 =+ ¢pI1=11 (8.13)

Write

Substitute (8.14) into (8.13) to get

<-_ _ _ + 4

(8.14)

(8.15)

which, using (8.4),is written as

fz2 <_ -a2 V2 + p2 (8.16)

where

0' 2 _--

P2 --

(7 - ix - Cpe-2)

/-,5

CpJ

4

(8.17)

(S.lS)

31
Nowwrite

V2+a2V2 < P2

d <

1\

dt

Integrate this last equation to get ....

Vs(t, AO(t), Ag(t)) _e -a2('-t°) [V_(to, AO(to),_(to)) - #_s] + p--_2

O'2

Assume that the variation of the feedforward is small enough in the sense that (8.10) holds such that

as > 0 for e2 > e_ where

2 ¢'P

ErrL --"

- Cx

Define the supremum of Vs over t as

I72 = max V2(to, AO(to), A}(¢o)),_-£2

Since V2 is positive definite, this implies that the state remains bounded, i.e. we obtain Lagrange stability.

Also, convergence to the set {AO(t),AO(t): V2 _< _} with rate of _t least as is guaranteed. Note that

choice of a large E guarantees a fast convergence to a large set, while choice of a smaller e > e,_ > 0

does not guarantee such a fast convergence but guarantees convergence to a smaller set, i.e. the state

converges closer to zero.

Following closely this approach tends to give conservative bounds on ][e[[. In practice, one may evaluate

explicitly V2 using (8.6) and use directly V2 and _'2 to find relationship (8.16) and hence obtain less

conservative bounds.

8.2 Exponential stability of canonical feedforward

Uaiform asymptotic stability of the system under the canonical feedforward With any strictly passive

BIBO stabilizing controller implies local exponential stability. However, it is difficult to explicitly construct V1 in (8.1,8.2,8.3) in general. One case for which we can explicitly construct V1 is if there is

damping at the links, case that we will consider here.

We do not provide a demonstration for a general strictly passive controller Cv but for a simple case, i.e.

Cv(BTAO) = KvBTAO (the global controller structure is feedforward with proportional-derivative (PD)

controller). Also, for the demonstration, we will assume that only a linear damping term, DoAg, appears

in the error equation (see (6.7)).

Proposition 8.1 Consider a flezible joint manipulator with linear damping at the links Dlo > O, Assume

that feedforward FF4 with C represented using Christoffel's symbols or feedforward FF6 is used. Also,

close a static state error Cp(yp) = KpBT A8 and a strictly passive BIBO controller loop C,(BT A_) =

KvBT AO where the gains are chosen to satisfy Assumption 6.1 and the following assumption:

Assumption are chosen such that [Do + BK_B Tj > 0. ...... _ ...... _ : .

8.1 K.

Then, for Od su_ciently small in norm, the zero error equilibrium point is locally (to A8 = O) exponentially

stable.

Proof • Consider the following energy based Lyapunov function candidate where the last term is used

to cancel extra terms introduced by the cross term in ix9 and _:

32

J

I

m

I
I

F

J

w

W

= ==

w

w

=

m

m

m

b ,,

Evaluate the time derivative of V1 along the solution of the system given by its error equation ((6.9) for

FF4, and (6.12) plus the stabilizing loop for FF6) to get, using the properties listed in Sections 3.1 and

3.3,

for FF4 where used the skew-symmetry property of (1⁄2/1)/(0, 0) - C(8,0)) and we assume u2 = 0, and

-t-cAO T [2I_[(0, O)AO -- Co(O, O)AO -- -_ MD(O, O)Od 4" _ MD( O,

-_0_[D0+BK_B_-_M(0)] _0+(J +_0_)Bu_ (S.2,)

for FF6 where we used (3.25) and also assume u2 = 0.

We now proceed to show that V1 and _'1 satisfy (8.1,8.2,8.3,8.4).

Lower bound of V1 (8.1) : From (8.19), we have

1 1

v, > _ (_ + _.)IIAOll _ - _M IIAOII/'0 + _M IIZ_O[_

for c >_ 0. Write the cross term as

such that, using (8.23) in (8.22), V1 satisfies

v, >_ _,ll_Oll =+ ,_=l_ol=_>Ii_ii =

where

1 CTMe21 )

a = min{al,a2}

_-,eM_-,r_llAol _]

1

Proof : of the last inequality. Write, for the 2 - norm, (we assume al and a2 nonnegative)

ol 02 o2 v_Ao > mi_ {_, _=}I1_11_

which concludes the proof.

To guarantee positive definitiveness of V_, we impose al > 0, a2 > 0 yielding

__ oLp 4- COtv CTM < C.2 <

OtM C"[M

which, for a nontrivial solution requires

otp 4- cvt v cTM <

vt M c 7 M

(8.22)

(8.23)

==:

33
This is satisfiedfor c small enough, i.e. 0 < c < c_, where

C_ _ (8.24)

Upper bound of V1 (8.4) : From (8.19), we have

Write

1 1

<_ + i oll_<_ IIx[I2

such that, using (8.26) in (8.25), Vt satisfies

where

1 (_p At- ¢_'v 3t" C')'M e2) >0

I¢ = max {S:l,S_2}

(s.25)

(8.26)

Proof : of the last inequality. Write, for the 2 - norm,

v_o < maz {_,, _} ]]zll_

which concludes the proof.

Hence, V1 is upper bounded for any bounded c. :

Upper bound of V,_0V1 (8.3) : V1 being lower and upper bounded by quadratic functions, and continuous, the norm of the gradient of V1 with respect to its argument is also bounded. Evaluate explicitly

the gradient of V1 with respect to A0 from (8.19) :

VAt_V,(A0, A0) -- cM(0)A0 + .M'(0)A0 (8.27)

such that

where

/32 - 7M > 0

Proof : of the last inequality. Write, for _1 and f12 nonnegative,

which leads directly to the bound in _.

Upper bound of V'_ (8.2) : Use (8.20) and property (3.33) to obtain, for FF4,

(s.28)

34

m

i

I

[]

i

i

z

B

D

m

i

i

m

i

B

i

I

i

!

I
u

and use (8.21) and property (3.27) to obtain, for FF6,

3 3 A_I[

Identify 71 with 7c2 in (8.28) and 7z with 37D in (8.29) to write, for both FF4 and FF6,

_,<-_o_,,_,,_+,"_°"I_ I_+_,"_" I_ I_I-Ion-_,I_01_

We willuse thislastequation to findconditionsforthe negativedefinitivenessof 171.

For negativedefinitivenessof I_'l,we firstneed

cap > 0 and a,_ - CVM > 0

Due to Assumption 6.1, this is satisfied for 0 < c < c2 where

_v

C2 =

"[M

(8.29)

(8.30)

(8.31)

=

w

w

E_

E =

m

=

L

i===

W

E: :

=

Then, use

to get

=T

Assume that HAOH is uniformly bounded above by 7e such that

a. --CTM --C?lTe > 0

_o__ lle_l _s_o,_orm,y _o_o_e_ _ovey_ _on

2, -._,,_0,,_ __llAol __-_Ilxll2

where

_,,,_0,, Aol_-[_-_M-_ =_11]_°11_

"}'/¢327d ot v "Yl= C [°t'- 2 ] ")'2=--C"fM--C'yl"/e--C_E32"yd

7 = rain {7,, 72}

where we assumed 71 and 72 positive. This last condition is satisfied and V1 is negative definite if

av -- C"/M -- C'TI'Ye "_"fd

C2_ 7 d a._p

a. - CTM-- c71% < "_Td

7d < 2 [ap(av--CTM--CTI%)] _

7I c

(8.32)

For a non trivial solution, we need

Hence, ")'dmust satisfy

35
Summary : Conditions (8.1,8.2,8.3) are satisfied if eX0 and tJd are small enough in norm and satisfy

relation (8.32) for

0 < c < rain {cv, c2}

where c_ and c2 are respectively given by (8.24) and (8.31). ,,

We may ascertain a rate of convergence larger or equal to a = 2" Note that a better evaluation Of the

convergence rate is given by a = min _f_, :zz lt.

l gl _2 J

Also note that (8.32) indicates that an increased supremum of the norm of tJa reduces the allowable bound

% such that the region of convergence decreases.

Moreover, we obtain similar qualitative results for both FF4 and FF6 and the effective difference would

show up in the evaluation of the bounds through 7I8.3 Stability for various forms of feedforward

We now conclude on the stability with the various forms of feedforward by using the results of the previous

section on the exponential stability of FF4 and FF6.

The variation terms e for the different forms of feedforward with respect to the canonical feedforward

depending on the canonical feedforward used, we will consider separately the analysis based on the

results for FF4 (representation of C using Christoffel's symbols) and for FF6 (representation of C using

M/p-notation).

NOTE : In order to obtain a better bound on the variation of I/2 than in (8.7), explicitly evaluate (8.6)

using (8.27) or equivalently by identifying Bu2 with e in (8.20) :

V2 _- _/1 At" ( c/NOT "1" AOT) e

< 1)_+/3' llzllIle[I

where we may use/3' = (c + 1). We then identify ( with/3' in (8.8).

8.3.1 FF4 as the canonical feedforward

The variation terms e for the different forms of feedforward with respect to the canonical feedforward

FF4 with C represented using Christoffel's symbols are given by

FF1. e =

FF2. e =

FF3. e =

FFT. e =

FF8. e =

[M(0d)- M(e)] gd + [Cc(Od,Od)- cc(e,o)] od+ _(ed)- e(e)

_(o_)- _(o)

-M(O)Od - Co(O, O)Od -- DoOa + _(0_) - f(O)

-M(O)Od - Co(O, O)Od -- DoO,_

We did not consider FF5 and FF6 because C is not represented by the same notation as FF4.

The norm of the variation e can be bounded above by

l

m

[]

m

I

I

_D

[]

[]

3

[]

m

g

i

m

m

m

m

il

III

m

36 !
t :

M

= ]

w

z

i

L =

r --

l

W

L

=

W

z

= (_-_+ _o_-y_ +_R)llaell +)_ aeI

where we used (3.26,3.34,3.35).

where we used (3.26, 3.34).

FF3. Ilell < _RIIA011

where we used (3.35).

_..,.., -<I1_<o)o_ +I_<°.°)°,11 +I_o°_I+ll_(o,)- _(o),

= _RII_ell + _c_d la_ + ('_,_ + _c_ + _o_)

where we used (3.31, 3.35).

_<_.1o_+_ o_I'+_oIo.II I1=oll +,o11o_1

where we used (3.31).

The variation of the feedforward signal e for FF1, FF2 and FF3 may be written in the form of (8.9),

while for FF7 and FF8 it may be written in the form of (8.12).

The conclusions on the stability and performance for the different forms of feedforward are summarized

in Table 1. Achievable type of stability, and qualitative conditions on design parameters for stability,

for fast rate of convergence and for smallness of the convergence set (for Lagrange stability) are listed in

the table. We recall that to guarantee stability, we require that a_ in (8.11) or a_ in (8.17) is positive

depending on the form of the bound on the error signal and that a faster rate of convergence is obtained

as these variables increase. For the Lagrange stability, convergence to a smaller set about the zero error

state is guaranteed as p2 decreases in (8.18).

8.3.2 FF6 as the canonical feedforward

The variation terms e for the different forms of feedforward with respect to the canonical feedforward

FF6 are given by

FF1. e = [M(0d)- M(_)]/_ + [CD(_,_)- Cm(_,0) + 1⁄2Mm@,_)] _ - 1⁄2Mm(_,&_)_+ _(_)- e(_)

FF2. e = [M(_d) - M(_)]_4 + [CD(_d,Od)- CD(8,_) + 1⁄2MD(#,_)] &

FF_. _ = 1⁄2Mo(_,_)& - 1⁄2Mo(_,_)_ + _(_) - _(_)

37
-FFI II

_FF2 II

[FF3 II

Condition for Type of

st ability: st ability

small enough

7d, 7rid, 7n) local exponential

(Old,_/dd) local, exponential

(TR) I 10cal exponential

-UF-4

FF7II

VrF8II

(Td, "/R)

(74) [ Lagra.nge

Condition for fast

rate of convergence:

small

(Td, 7dd, 7n)

(74, 7da)

(7n)

Condition for

convergence to small set:

small

(Td, Tad)

local exponential

Lagrange (Ta, 7dd, "/R) (Td, Tdd)

(Td,7dd)

m

w

m

U

m

I

Table 1: Stability and performance analysis, FF4 as canonical feedforward

FF4. e = 1⁄2Mo(O,O)Od - 1⁄2Mo(O, Oa)O

FF5. e = e(0a)- e(0)

FFT. e = -M(O)Od - [CD(O,O)- 1⁄2MD(0,0)] [_d-- 1⁄2MD(O, Od)O- DoOd + e(Od)- _(0)

rr8. e = -M(O)Od - [CD(0, t_)- 1⁄2Mo(0,0)] Od -- 1⁄2MD(O,O,_)[_ -- DoOd

The norm of the variation e can be bounded above by

FF1. lle[I _< It_(o,,)-,,:o)to,+1[co(o,,,o_.)- co(o,o)+_o(o,o)]o,- -_:,..,_(o.o,,),_ll +

IIf(Od)- _(O)ll

-- ( _/ D _/ a d -_- -_ "[ D d _/d 4"

where we used (3.26, 3.30, 3.35).

_<_D11_,IIIAOlt + _-,,o,IIo,1'll,,,oll +_o Io, ,,o

(TDTad+ _TD "ed)ll_Oll+

where we used (3.26, 3.30).

FF3. Ilell < 11⁄2Mo(O,O)Od-1⁄2Mo(O'Od) _ + II_(0d)- _(0)ll

= 7allnOll + 707d Ino[

where we used (3.29, 3.35).

FF4. Ilell-< II-_Mo¢O,o)O,-_,"o(°,°,

_<_oo,1111_oll

- _D_,I"011

where we used (3.29);

FFS. Ilell <- 7all_xall

38

I

[]

[]

[]

[]

ii

E

!

I

m

II

I

m

Im

|

m

[]

|
where we used (3.35).

_J

w

m

u

r_

b

where we used (3.28, 3.35).

where we used (3.28).

The variation of the feedforward signal e for FF1, FF2, FF3, FF4 and FF5 may be written in the form

of (8.9), while for FF7 and FF8 it may be written in the form of (8.12).

The conclusions on the stability and performance for the different forms of feedforward are summarized

in Table 2. Achievable type of stability, and qualitative conditions on design parameters for stability, for

fast rate of convergence and for smallness of the convergence set (for Lagrange stability) are listed in the

table.

FF1

FF2

FF3

FF4

FF5

FF6

FF7

FF8

Condition for

stability:

small enough

("/d, ")'dd, 7n)

(Td, 7aa)

(Td, Tn)

(Td)

(Tn)

Type of

stability

"local exponential

local exponential

local exponential

local exponential

local exponential

Condition for fast

rate of convergence:

small

('i'd, 7rid,7R)

(Td, 7n)

(Td)

(Tn)

Condition for

convergence to small set:

small

local exponential

(Td, 7R) Lagrange ('i'd, 7dd, 7R) ("i'd, "fdd)

('i'd) Lagrange (Td, "/dU) ( Td, 7dd)

Table 2: Stability and performance analysis, FF6 as canonical feedforward

8.3.3 Summary

We see from Tables 1 and 2 that the conditions for stability and fast rate of convergence with the different

forms of feedforward involve the upper bound on three parameters that can be affected by design :

• Bounds (Td, TdU) are made small by using a slow desired output trajectory (Ot, and its higher

derivatives). These bounds are also affected by the system parameters and by the parameters of

the compensators through the solution of 0m_ and its higher derivatives (see Section 5).

• The bound 7n is made small by choosing R giving the best fit of the gradient of the spring characteristic in the sense that sup {liVer(0) - RII ) is minimized for R in the set of matrices meeting

the design requirements (Sections 5, 6 and 7). For a linear spring characteristic, this leads to

39
7n = 0 if we may choose [r(_)) - R8] = O. Then, odd and even numbered feedforward forms become

equivalent.

9 Conclusion

We have presented a controller design approach based on passivity and Lyapunov stability theory for the

tracking of flexible joint manipulators. The overall design procedure may be viewed as consisting of two

main steps described below.

Firstly, compensators are designed by taking advantage of the inherent passivity properties of flexible

joint manipulators. The procedure for the design of the passivity based controller involves essentially

the formation of a passive and zero-state detectable error system by use of feedforward compensation

and static state feedback, and the asymptotic stabilization of the system by using any strictly passive

controller with finite gain. However, the feedforward compensation, which is based on plant inversion,

may not have a causal solution, or it may be very difficult to solve for a stable inverse of the system due

to its nonlinear nature, such that implementation of the controller is then compromised. Also, certain

terms of the dynamical equation may have a negligible effect on the overall dynamics of the system, e.g.

Coriolis and centrifugal effects at low velocities, such that we might want to neglect these terms in the

feed forward.

In the second step, we use stability results from the passivity based design_ i.e. uniform asymptotic

stability of the system implies local exponential stability, to analyze the stability of the system with

various feedforward compensation. To do so, we conduct a Lyapunov based robustness analysis with

respect to approximations in the feedforward compensation. This analysis allows to ascertain local

asymptotic stability or Lagrange stability of the system under certain conditions involving bounds on

the parameters of the system, and on other parameters that are affected by design such as the rapidity

of the desired output trajectory. In order to obtain quantitative results, i.e. obtain numerical values for

the bounds, we must explicitly construct a Lyapunov function (local) for the system, which may impose

additional constraints on the system (we required the presence of damping at the links in our analysis).

This design approach may be applied to both rigid robots and flexible robots. A particular case where this

approach may be particularly useful is for the controller design of flexible joint robots with small, unmodeled or badIy known gyroscopic effects, so that inclusion of these effects in the feedforward compensator

is not practical.

Future work will tackle both theoretical an practical problems encountered in the control of flexible joint

robots, and of flexible structures in general. Plans include

• Analysis of the active use of the link state measurement in the design framework presented here in

order to improve the performance of the system.

• Design a nonlinear observer to estimate the link state based on the motor state measurement, since

measurement of the link state is not available in general.

• Design a saturation-driven trajectory generator in order to maintain stability and performance of

the system in the presence of hard constraints, in particular of input torque saturation.

Pursue the validation of the results.

m

r _

U

m

m

!

m

R

m

m

m

m

[]

m

[]

m

i

i

m

|

m

[]

m

m

m

m

m

=

!

il

,_o i
w

A

Appendix

Centrifugal and Coriolis matrix for M12 strictly upper triangular and symmetric

motor inertia

_2

F

u

w

In this section, we present the equations for the Coriolis and centrifugal matrix for flemble joint robots

with symmetric motor inertia, and with the matrix of gyroscopic couplings Mn strictly upper triangular

and having the following structure

0 ml,2(Oea)

0 0

Mx=(0t) = :

0

where mij are scalar functions.

ml,3(Oe,l,0e,2) "'" ml,m(Otl,''',Ot,m-1)

m2,3(0t,2) "'" m2,m(Ot2,''',Ot,m--1)

0 ... 0

We recall that the mass matrix of the system can be represented as follows

M(Oe) = [ Mn(Oe) MT(oe) M:2(Oe) M22 ]

and note that we will use0= [Oe T OmT ] with 0e, 0m ER m.

We will see that we may factor the Coriolis and centrifugal matrix as

C(Ot-'Orn'Ot'Orn) = [ C1Al(O_''oe) cAl(Oe, 2cC1Bl(Ot''om) Oe) Ci42(Oe' 0 t}t) ]

where the individualterms of thismatrix are definedbelow.

A.1 Equations for C represented using Christoffel's symbols

We recall that the coefficient of the Coriolis and centrifugal matrix are given by (see Section 3.1)

c_ = _ _ [---gU+ ooj -_ ]

i---1

(A.1)

First, restrict (1 _< k < m), (1 < j _< m) in (A.1). After simplification due to the dependence of MH,

M12, M T uniquely on 0e and due to the fact that M22 is constant, we can rewrite the equation as

c,,,(k,j_ = c_a,(o_, oe)+ c_(o_,o_)

with

1 [OM,,,(_,s)(oe) OMn,(k,i)(Ot) OMn,(i,j)(Oe)

cA(o"O') = _-'_ I _ + oo,,j - oo,_

iml

C_(O_,Or.)=__L1 [01_12,(k,i)(Ot)_ -- OMT'(i'J)(O¢)]_-_kl,k J Om'i

i=l

where we recognize CAI(Ot, Oe) as the Coriolis and centrifugal term of the rigid robot, and CIBI(Oe, O,n) is

skew-symmetric and has the following structure

0

-cMOe, x,..., oe,_-,; L,,.a,..-, Ore,m)

c_(o_,O,,,) =

-cx(__2)(0e,,,""", 0e,m-, ; 0_,m-l, 0_,m)

--Cl(m_l)(Oe,l,'" ", Oe,m-1; Ore,m)

0

0

-c_(__ ,.1(0,,2, . . ., Oe,,,-, ;L,,,,,,-_, 0_,,,,)

-c_(,,,_x )( Oe,2, . . ., Ot,..-l ; 0,,,,_)

0

41
c,3(e_,,, ••., 0_,.,_:; 0m,4,•.., _im,.,)

C23(0t,2, " " " , 0_,m-1; 0m,4, " " ", _m,m)

--C3(.,_2)(0_,3,''-, 0_,.,_: ;0.,,.,-:, 0.,,,,,)

--C3(.,-I)(0_,3, """0_,,,,__ ;0..,.,)

0

"'" Cl(m--1)(0g,l,''',0g,rn--1;bm,m) 0

• "- C2(m_1)(0£,2,''' , 0_,m_l; bin,m) 0

• .. C(,_-2)(m-:)(0_.,,-2,0_,m-1;0,,,,m) 0

• -- 0 0

--- 0 0

where cij are properly defined scalar functions. We note that for i = 1,..-, m, row i of C_ does not

depend on any 0m j, j _< i.

Now, restrict (1 _< k _< rn), (m < j _< 2rn) in (A.1). After simplification, and defining p = j - m, we can

rewrite the equation as

A

C12,(k,p) = C12,(k,p)

m l[OM12,(k,v)(Oe) OM12(i,,)fOe)]

i=1

We note that for k >_ p, the elements of the matrix are zero such that this matrix is strictly upper

triangular.

Now, restrict (m < k < 2m), (1 < j < m) in (A.1). After simplification, _Lnd defining p = k - m, we can

rewrite the equation as

A

C21,(p,j) = C21,(p,j)

OMT("i)(Oe)] O,,i

= _-- -2I "OMT("J)(Oe) aoe,i + _ j

i=:

We note that for j > p, the elements of the matrix are zero such that this matrix is strictly lower

triangular.

Now, restrict (m < k < 2m), (m < j < 2m) in (A.1). After simplification, we obtain

C22 : 0

A.2 Equations for C represented using MD-notation

We recall that the coefficient of the Coriolis and centrifugal matrix are given by (see Section 3.1)

C(O,O) = MD(O,O)- 2MT(O,O)

MD(O,v) = _-_OM(O)veT

i=l OOi

where ei is the i th unit vector in It".

Vsing(A.3)' we obtain

[ M_,ll(Ot, MA.:I(Oe, Oe) + M_,,l(Ot, Or) Om) 00 ]

where

m O li_Ill(Oe)OeernTi

i/Ll(O,,°e) = E ooe,,

i=1

42

(A.2)

(A.3)

m

g

=

I

ill

g

B

I

z

i

=_

i

Z

[]

i

[]

u

M

mm

I

I

[]

I

[]
w

w

w

= ,

L

R

w

M_,I 1(0,, 8,-n)

: !

M3,21(St, St)

"_ 0M:2(8,)8 em T

i=1

m OM_(Ot)d em r

i=l

where erni is the i *h unit vector in It".

Furthermore, using (A.2), we have

cA(0,,0,)

1 A T

1 B T.-

= MB,11(0,,0") - (ut,0m)

1 A T

= -_MD,21 (Oe,8,)

= MA,21(Oe, Oe)

We note that c1AI(st, Oe) is the Coriolis and centrifugal term for the rigid robot. Also,

mOB ll(0_,_m.) =

mdll(O,,l,'",O,,_-l;_",2,'",g",")

0

0

0

rndl3(St,1,'",8e,m-1;OmA,'",_m,")

md23(SI,2,'",Ot,"-l;_m,4,'",Srn,")

0

0

mdi2(8_.l,'", Or,m-l; _",3,''', _",m)

md22(01,2,''', Or,m-l; _",3,'" :, 0",")

0

0

•'' mdl("_l)(Se,1," ",Be,m-I; _.",m)

• '' md2("_l)(Ot,2," ", Ot,"-l; 0".")

:

• .. md("_l)("_l)(O,,m_l; 0",")

• • " 0

0

0

0

0

where md 0 are properly defined scalar functions, so that for i = 1,..., m, row i of C1BI(8,, 8") does not

depend on any 8,n,j, j _< i.

We also note that MA,21(_,, _) is strictly lower triangular such that C1A2(8,, _e) is strictly upper triangular,

and cA(6t, 0_) is strictly lower triangular.

NOTE : We note that the two representations for C considered here yield a Coriolis and centrifugal

matrix with the same structure and characteristics.

B Feedback equivalence of flexible joint robots to passive systems

Here, we want to analyze the property of feedback equivalence of fle_ble joint robots to passive systems.

To achieve this, we first present some definitions and results extracted from [5] and then apply the

theorems to flexible joint robots.

B.1 Feedback equivalence of nonlinear systems to passive systems

Consider a nonlinear system E described by equations of the form

= f(x) + g(x)u (n.1)

y h(x) (B.2)

43

w
with state space X = R q, set of input values U = It' _ and set of output values Y = pm. The set L/

of admissible inputs consists of all U-valued piecewise continuous functions defined on R. f and the m

columns of g are smooth (i.e. C ¢¢) vector fields and h is a smooth mapping. We suppose that the vector

field f has at least one equilibrium; without loss of generality, we can assume f(0) = 0 and h(0) = 0.

Definition B.1 By regular static (i.e. memoryless) state feedback, we mean a feedback of the form

u = a(z) + fl(x)v (B.3)

where a(x) and/3(x) are smooth functions defined either locally near x = 0 or globally, and j3(x) is

invertible for all x.

Definition B.2 The system represented by (B.1, B.2) is feedback equivalent to a passive system if there

exists a regular static state feedback (B.3) such that the closed loop system

is passive.

= [f(x)

y = h(x)

Definition B.3 A system of the form (B.1) is said to have relative degree {1,-.., 1} at x = 0 if the

matrix Lgh(O) is nonsingular.

In Definition B.3, we use the following notation [13]:

q..L Oh(x) x

Lgh(x) )_2--EEF=g,( )

i=l

where gi(x) = Eig(x) with Ei E R mxm has element i, i equal to one, and all its other elements equal

zero.

The relative degree is also equal to the smallest order of time derivative of the output in which the input

appears explicitly.

Definition B.4 The distribution A spanned by the vector fields gl(x),'",g,,(x) is involutive if, for

gi,gj E A, the Lie bracket

[gi, gi] z_ Ogj gi Ogigj

= -_z - -_x E A for i, j = l, . . . , m

This is equivalent to say that

rank {gl(x),"-,gm(x)} = rank {gltz),'",gm(x),[gi,gj]} for i,j = 1,...,m

I

[]

=

l

l

J

[]

m

Ill

I

I

D

=

B

Ill

g

J

[]

If system (B.1,B.2) has relative degree {1,..., 1} at x = 0 and the distribution A spanned by the vector

fields gl(z)," .,g,_(x)is involutive, it is possible to find q- m real-valued functions Zl(Z),---,zq_m(x),

locally defined near x = 0 and vanishing at x = O, which, together with the m components of the

output map, qualify as a new set of local coordinates. In the new set of coordinates (z, y) the system is

represented in its normal form

._ = q(z,y) (B.4)

il = b(z,y) + a(z,y)u (B.5)

where the matrix a(z, y) is nonsingular for all z, y near (0, 0).

I

I

I

[]

44 M
L

i

The zero dynamics of a system describe those internM dynamics which are consistent with the external

constraint y = 0. If a system has relative degree {1,...,1} at x = 0, its zero dynamics exist in a

neighborhood X ° of x = 0, evolve on the smooth (q- m)-dimensional submanifold

z"= {x :h(x)=0}

and are described by a differential equation of the form

= f'(z) z _ z"

in which f'(x) (the zero dynamics vector fields) denotes the restriction to Z" of the vector field

f'(x) = f(x) + g(x)u" (x)

with

u" (x) = -[Lgh(x)] -1Lfh(z)

In the normal form (B.4,B.5) the zero dynamics are characterized by the equation

= q(z, 0)

In order to have a globally defined normal form, the following conditions must be satisfied:

H1 : the matrix Lgh(z) is nonsingular for each z E X,

H2 : the vector fields _l(x) ...._m(z) (defined below) are complete,

H3 : the distribution spanned by g_(x) ... gin(x) is involutive,

where

[_l(x) ..-_m(x)] = g(x) [Lgh(x)] -_

Note that by complete, we mean that the integral curves are defined for all t _> 0 for any initial conditions.

Definition B.5 A nonnegative function V : X _ R is said to be proper if for each a > O, the set

v-l([0,a]) = {_ • X : o < v(_) < a) is comp.ct.

Definition B.6 Suppose Lgh(O) is nonsingular. Then _ is said to be:

i) minimum phase if z = 0 is an asymptotically stable equilibrium of q(z, 0),

ii) weakly minimum phase if there exists a C k, k >_ 21function W'(z), defined near z = 0 with W'(O) = O,

which is positive definite and such that Lq(:,o)W'(z) < 0 for all z near z = O.

Suppose H1, H2 and H3 hold. Then E is said to be:

i) globally minimum phase if z = 0 is a globally asymptotically stable equilibrium of q(z, 0),

ii) globally weakly minimum phase if there exists a C k, k > 2, function _V'(z), defined for all z with

W'(O) = O, which is positive definite and proper such that Lq(z,o)_V'(z ) < 0 for all z.

Definition B.7 A point x ° is a regular point for a system E of the form (B. 1) if rank {Lgh(x)} is

constant in a neighborhood of x °.

45
Also,weassumethat rank {g(0)} = rank {dh(0)} = m.

We can now state the main theorems relating feedback equivalence of nonlinear systems to passive

systems.

Theorem B.1 [5] Suppose z = 0 is a regular point for E. Then _ is locally feedback equivalent to a

passive system with a C _ storage function V, which is positive definite, if and only if _ has relative degree

{1,.-., 1} at x = 0 and is weakly minimum phase.

Theorem B.2 [5] Assume H1, H2 and H3. Then _ is globally feedback equivalent to a passive (respectively, strictly passive) system with a C 2 storage function V, which is positive definite, if and only if

is globally weakly minimum phase (respectively, globally minimum phase).

B.2 Flexible joint manipulators

We now find the conditions under which a manipulator with flexible joints is feedback equivalent to a

passive system, i.e. under which Theorem B.1 or B.2 is satisfied.

First consider the general model of a manipulators with flexible joints (3.10) repeated here for completeness

[ Mll(O_, e,,,) M12(O_, 0._) _J_

[ C11(e_, C21(o_,e._, e.,, de, de,_m) Ore) C22(0_,0._, o_,hm) om +

u (B.6)

First, write (B.6)in the form of (B.1,B.2), i.e. define

3; = [TTTT] x 1 x 2 x 3 x 4 T

zl = _t z2 = _,,, z3 = el z4 = e,,,

P2 C21 C2_ x_ Dm + r2

where the state dependence has been dropped, and use the fact that Mll is nonsingular (in fact so is

M22) to write the inverse of the mass matrix as

M-1 _ [ M51 + M51M12A21MT M51

- [ --A_IMTMH I

where

1,_= M_ - MrM5 'Mi_

We may then write, after some simple manipulations,

_2

_3

._4

-[MS 1 + MS'M12A;1MTMH '] Pl + MH'M,2A_'p2

A_I MT MSl pl - &2-1P2

Xl

2_2

"M_lM12A_ 1

+ _

Or_ × r_

Om×m

u

Consider the case where the output is a linear combination of the state and is given by

y=h(x)= [ C, C Ca C4 ]

z

46

I

m

N

M

Illl

Wm

1

[]

B

I

II

I

m

!

g

i

m

I

m

m

m

I1

m

m

i

M
I.--

where C1, C2, C3, C4 E R. mx'n are constant.

We readily verify that rank {g(0)} = m for any arm configuration since A2 is always nonsingular. We

will impose the constraint on h(x) that rank {dh(0)} = m, i.e. the vector [ C1 C2 C3 C4 ] has rank

m.

First, find conditions on the output matrix such that the system has relative degree {1,..., 1} at z = 0.

Write

L_h(x) - Oh(X) M5IMl_A_lOxl + _A_Oh(x) 1

= [-C,M_'M,_ + C2] A_'

Fact B.1 Since the matrix A 2 is nonsingular for any state value, the system has relative degree {1,..., 1}

at x = 0 if and only if the matrix • = [-C1M['I 1M12 + c2J

r 1

is nonsingular for T, O.

NOTES :

2

=

For any arm configuration, the presence or absence of the motor and link positions in the output

does not affect the conclusion of Fact B.1.

If there is no gyroscopic coupling, i.e. M12 = 0, then C2 must be nonsingular, i.e. the motor

velocities must appear at the output, and C1 is arbitrary.

If M12 is nonsingular for any state value, choosing the output as the link velocities with C1 nonsingular is sufficient to guarantee that • is nonsingular since Mll is nonsingular for any state

value.

If M12 is not zero at x = 0, then (I>(0) is singular only for specific combinations of C1 and C2.

The point x = 0 is regular if rank{'_} = m in a neighborhood of x = 0. This is guaranteed by the

continuity of Mll and M12 if (I) is nonsingular at x = 0. This holds globally (condition H1) for proper

choices of C1 and C2 for any arm configuration due to the boundedness of MH and M12, i.e. if we choose

C1 and C2 such that

sup O'maz{C1M,À (x3, x41-" MI2(X3, X4)} < amin {C2}

This holds in particular if C1 = 0 or M12 = 0, and C2 is nonsingular.

We now proceed to show the (weak) minimum phase property. The zero dynamics of the system are

described by the following equation :

= f(x) - g(z)[L.h(x)]-' Lfh(x) x Z"

= S(x - [ c, c3 ] S(xi x • z"

assuming that • is nonsingular in the neighborhood of x = 0. After some manipulations, this leads to

- [MI_ 1 + M51M12_-ICIM_I i] P! + MH1M12 _-1 [Caxl + C4x2]

= ¢-IC1M_Ipl _ _-1 [C3Xl + C4z2] z E Z"

£'1

x2

We consider two cases here while keeping in mind that we must satisfy (I) nonsingular. We define the

output as a. function of the motor state only for the first case, and of the link state only in the second

case.

47
Case 1 : C1 = C3 = 0, C2 nonsiugular, C4 nonzero.

The dynamics on the zero manifold are described by

-M_l pl + Ml'll M12C21C4z2

-C_IC4zz

:_ =

T, 1

Z2

x E Z" ._- {x E X° : C2_,2 2vC4x4 -- O}

I

=

I

Setting the output to zero, we get

C2x2.4_ C424 = 0

X2 "-- _C21C4.T4

Hence, if C_1C4 > 0 then z4 ---, 0 exponentially; if C4 = 0 then z4 is a constant; in general, x4 --, null

space of C_" 1C4 _> 0.

NOTE : With the output of the system defined as the output of a exponentially stable first order

exosystem with the motor position as its input, zero system output implies that the motor state goes to

zero. If the exosystem is just stable, then we can only guarantee that the motor position will go to a

constant.

Given that the motor position x4 converges exponentially to a constant x41 and the motor velocity z2 to

zero, the question of stability of the zero-dynamics resumes to analyze the stability of

This equation has the same stability properties as a rigid robot with a constant forcing term in z4y.

Hence, consider the following scalar function

V(z1, x3) lxrMl,(z3,x4j>, + U(x,,z3) (B.T)

U(xl,x3) : jfoXa rl(_,x4l)d_

U(xl, x3), and thus V(xl, x3), is positive definite if the equivalent torsional spring is strong enough with

respect to gravity, i.e. if

NVxakl(x3, x4.f) > -_'zage(x3, x4i) Vx3, x4f E R rn

for rl defined as in (3.11) with k_(x3, x4) monotonically increasing in x3, and k,(0, 0)= ge(0, 0) = 0.

Represent the centrifugal and Coriolis matrix using Christoffel's symbols such that (_M1 "- C) is skew

symmetric, which also imply that (1⁄27_fll - C!l) is skew symmetric. Then, take the time derivative of

(B.7) to obtain

V(x,,z3) =

such that, for positive or at least non negative damping, V <: 0. Hence, we conclude that the zero

dynamics of the system are stable, and that the system is at least weakly minimum phase (Definition

NOTE : If there is positive link damping then we conclude that xl -, 0 as well as its higher derivatives,

such that the system equation becomes

..... ...... _ =

0 = rl(za,x<r)

m

i

m

I

I

i

B

I

I

w

g

m

g

m

m

m=

m

m

m

i

m

m

48 i
K.w

which defines the steady state value(s) of 23, i.e. the system is stable and weakly minimum phase.

With or without damping, if C4 is nonsingular such that 241 = O, then the zero state is asymptotically

stable such that the system is mlnimum phase (Definition B.6).

In conclusion, the conditions of Theorem B.1 are satisfied, i.e. the system is locally feedback equivalent

to a passive system, for the output containing only the motor state, i.e. if

y = C2_m + c4e.,

• if and only if

• rank{C2} = m such that rank{dh(O)} = m, and such that the system has relative degree

{1,..., 1} and is regular at any point in the state space,

• c_lc4 > 0, i.e. stable exosystem,

• and if the spring is stiff enough compared to gravity and the link damping is at least positive

semi-definite such that the system is weakly minimum phase.

? -

I

_r

W

F

Case 2 : C2 = C4 = 0, C1 nonsingular, C3 nonzero.

We must first assume that M12 is nonsingular to guarantee that (I, is invertible.

The dynamics on the zero manifold are described by

-C_1C321

-M_21pl + M_1MllC_lC3xl

21

22

X E Z* -_- {x E X° : C121 _-C323 _ O)

Setting the output to zero, we get

Clxl + Cax3 = 0

2, = -CllC3z3

Hence, if C_1C3 > 0 then 23 -- 0 exponentially; if C3 = 0 then :r3 is a constant; in genera/, z3 ---, null

space of CllC3 > O.

NOTE : With the output of the system defined as the output of a exponentially stable first order

exosystem with the link position as its input, zero system output implies that the link state goes to zero.

If the exosystem is just stable, then we can only guarantee that the link position will go to a constant.

Given that the link position 23 converges exponentially to a constant x3f and the link velocity 21 to zero,

the question of stability of the zero-dynamics resumes to analyze the stability of

= [ -Mh_(z3f'

21 = 0; x 3-- x3I

24) [C12(x31,2224, 0, 22)22 -_- rl(xaj', 24)] ]

Hence, the stability of the zero dynamics of the system depends on the stability of the gyroscopic coupling

subsystem (note that M12 may be positive definite, negative definite or indefinite (and also zero)). If

this subsystem is stable, then so are the zero dynamics. Wit'h the assumption that rl is defined as

previously and that k1(23,24) is monotonically decreasing with 24, this imposes the condition that

M12(23.¢, 24) > 0 Vx3], 24 E R m. Also note that if the motors have symmetric inertia, C12 is independent

49
of z2 and is thus zero in this case such that we can get Lyapunov stability only (which is still sufficient

to guarantee the system to be weakly minimum phase).

In conclusion, the conditions of Theorem B.1 are satisfied, i.e. the system is locally feedback equivalent

to a passive system, for the output containing only the link state, i.e. if

y = C10e + C30e

if and only if

• M12 is nonsingular,

• rank{C1} = m such that rank{dh(O)} = m, and such that the system has relative degree {1,..-, 1}

and is regular at any point in the state space,

• C_ICa > O, i.e. stable exosystem,

• the subsystem representing gyroscopic coupling is stable.

We have established some conditions to obtain feedback equivalence to a passive system by using uniquely

the motor or the link state. The same method may be used to analyze other choices of output, but this

analysis is not carried out at this point due to the difficulty in verifying the stability of the reduced

system describing the zero-dynamics.

In case 1 previously presented, the minimum phase or weak minimum phase properties are in fact global.

No such conclusion can be drawn for case 2 under the actual assumptions. Also, the two previous cases

also satisfy condition H1. Hence, only conditions H2 and H3 have to be verified to obtain global results

using Theorem B.2.

To verify H2, evaluate _(x) :

-M_t M12,12-1

_-1

0

0

such that the integral curves are defined for all t _> 0 and for all initial conditions provided that • is

nonsingular over the complete state space.

To verify H3, note that the Lie bracket [gi,gj] = O, Vi,j = 1,...,m so that (B.4) is verified, i.e. H3 is

satisfied without additional conditions.

±

Hence, in order to conclude on global equivalence, the only additional condition to verify with respect to

the local case is the global asymptotic or global stability of the zero dynamics, e.g. conditions regarding

damping in case 1 previously analyzed.

M

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i

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i

5O i
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m

[]

m

|

m

mm

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|

I

m

m

m

II

m

m

m

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i

m

ii

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