# H

Authors: Renan Lima Pereira; Karl Heinz Kienitz
Journal: Journal of Control Science and Engineering
 (2015)

---

## Abstract

This paper proposes a gain-scheduling control design strategy for a class of linear systems with the presence of both input saturation constraints and norm-bounded parametric uncertainty. LMI conditions are derived in order to obtain a gain-scheduled controller that ensures the robust stability and performance of the closed loop system. The main steps to obtain such a controller are given. Differently from other gain-scheduled approaches in the literature, this one focuses on the problem ofH∞ loop shaping control design with input saturation nonlinearity and norm-bounded uncertainty to reduce the effect of the disturbance input on the controlled outputs. Here, the design problem has been formulated in the four-block H∞ synthesis framework, in which it is possible to describe the parametric uncertainty and the input saturation nonlinearity as perturbations to normalized coprime factors of the shaped plant. As a result, the shaped plant is represented as a linear parameter-varying (LPV) system while the norm-bounded uncertainty and input saturation are incorporated. This procedure yields a linear parameter-varying structure for the controller that ensures the stability of the polytopic LPV shaped plant from the vertex property. Finally, the effectiveness of the method is illustrated through application to a physical system: a VTOL “vertical taking-off landing” helicopter.


---

## Body

## 1. Introduction

In recent years, input saturation and model uncertainty problems have been extensively studied in the control system literature, where much attention has been focused on the problems of robust stabilization and performance [1–9]. Input saturation is a phenomenon due to inevitable physical limitations of the actuators, such as pumps or compressors that have finite throughput capacity and motors that have a limited range of speed [10]. This saturation can lead to deterioration of the actuator itself or even to the instability of the system. When a system is subject to input saturation, two main issues arise: the guarantee of stability and the containment of performance degradation [7]. To solve this problem, there exist two approaches: two-step and one-step designs [7]. In the first approach, called antiwindup design, a predesigned controller is given without considering the input saturation constraints; usually a standard controller is used. Then, after this controller has been designed, an antiwindup compensator is designed to handle the input saturation nonlinearity. Thus, the antiwindup compensator is designed to ensure that stability is maintained and the performance degradation is contained. Antiwindup compensator research using linear matrix inequalities has been vigorously pursued as can be seen by a significant number of referenced papers [2, 11–14]. This is because LMI techniques offer the advantage of operational simplicity when compared to other approaches. For the second approach, one-step design, the input saturation is directly accounted for in controller design; that is, the controller and an antiwindup compensator are simultaneously computed [15].Several solutions for input saturation problems have been proposed. However, just few references deal with LPV systems [3, 16–18]. Motivated by this scarcity, recent work focused on employing gain-scheduling controllers designed with an H∞ approach [19]. This resulted in an advantageous technique using LMI for an H∞ loop shaping controller design with input saturation, derived from a four-block configuration. Its advantage lies fundamentally in the ease of making trade-offs between performance and robustness to plant uncertainty and the saturation nonlinearities are described as perturbations to normalized coprime factors of the shaped plant.As gain-schedulingH∞ loop shaping control can be viewed as robust against nonparametric uncertainties, this controller can also be viewed as a candidate to solve the robust control problem against presence of both nonparametric and parametric uncertainties. Thus, the contribution of this paper, motivated by the results in [19], consists in extending their procedure in order to ensure the robust stability of the closed loop system subject to both the constraint of an input saturation nonlinearity and parametric uncertainties. Here, the parameter uncertainties are assumed to be time-varying but norm-bounded. Sufficient conditions for the existence of the gain scheduled parametric H∞ loop shaping controller are given in terms of an LMI framework, which also provides the Lyapunov matrix ensuring the stability and robust performance of the LPV controlled system from of the vertex property [20, 21]. The effectiveness of the design method was evaluated using a physical system: a VTOL helicopter [22]. This paper is organized as follows. Section 2 gives some problem statements and preliminaries about the purpose of the paper. In Section 3 a solvability condition for existence of a gain scheduled H∞ loop shaping controller using the constraint of input saturation and norm-bounded parametric uncertainty is established. In Section 4, an example is considered to evaluate the effectiveness of the proposed method. Finally, Section 5 contains the conclusion.The notation used in this paper is fairly standard:Rn×m denotes the set of real n×m matrices and In is the n×n identity matrix. M>0 (or M<0) means M is symmetric and positive (or negative) definite. •  indicates symmetric blocks in the LMIs. Here, the notation(1)
G
s
≔
A
B
C
Dis used to denote the transfer function G(s)=CsI-A-1B+D.


## 2. Problem Statements and Preliminaries

The exposition in this section closely follows [19], where the loop-shaping design procedure based on H∞ robust stabilization combined with a four-block configuration is presented. In Figure 1, the block diagram for the H∞ loop shaping control design problem with input saturation nonlinearity is shown depicted. In this formulation, the input saturation nonlinearity is incorporated through a dynamic weighting function W, and the postcompensator is considered as an identity matrix with proper dimension.Figure 1
Block diagram forH∞ loop shaping control using four-block configuration with input saturation.In the absence of input saturation constraints anH∞ controller K∞ is designed such that the closed loop system satisfies the condition Tzw∞≤γ:(2)K∞II-GsK∞-1IGs∞≤1εmax=γ,where εmax is the maximum achievable robust stability margin, Tzw is the transfer function matrix from the disturbances w1w2T to the outputs z1z2T, and Gs is the shaped plant. Herein, the designer knows the maximum input umax,j at the jth channel, that is, the control value that can be used without exceeding the limit of the actuator in channel j. The saturation nonlinearity is defined as follows, in normalized form: (3)satjuj=-1,uj<-1,uj,uj≤1,+1,uj>+1.Normalization always can be achieved by scaling each channel in G and W with the appropriate factor. As shown in [19] the maximum control input in each jth channel can be specified; a corresponding slope αj for each jth channel is also known. Without losing generality, this slope can be assumed to be less than or equal to 1. Define satj(uj)=θjuj with θj as (4)θj=1uj,uj>1,1,uj≤1,-1uj,uj<-1,where j=1,2,…,m and uj is constrained by -umax,j≤uj≤umax,j. It implies that 1/umax,j≤θj≤1 (αj≤θj≤1) and, for the multivariable system with m number of input channels, it can be written as sat(u)=Θu with Θ=diag⁡θ1,θ2,…,θm. It is observed that in the H∞ loop shaping using four-block configuration the shaped plant Gs can be represented as a polytopic system where the input saturation constraint is allocated between the dynamic weighting function W and the nominal plant G.For the existence of a gain-scheduled controller, the sufficient conditions proposed by [19] for saturated systems have been elaborated in the following theorem.Theorem 1 (see [19]).
Consider the nominal plantG and a proper weighting function W, with -umax,j≤uj≤umax,j being a known saturation bounded for each jth channel where j=1,2,…,m. Then there exists a shaped plant with input saturation nonlinearity that can be represented as a polytopic LPV system: (5)
G
s
=
A
s
B
s
C
s
0
,where As,Bs,Cs is quadratically stabilizable and detectable for all Φ=(I-Θ) in the polytope Ω=∑i=12m‍λiξi:λi≥0,∑i=12m‍λi=1 with the vertices in ξ1,…,ξ2m. Moreover, there exists a stabilizing LPV controller K∞ such that γ≥1, if and only if there exist R>0, S>0 that satisfy the following inequalities: (6)
N
R
0
0
I
T
A
s
i
R
+
R
A
s
i
T
R
0
C
s
T
0
B
s
·
-
γ
I
0
0
I
0
·
·
-
γ
I
N
R
0
0
I
<
0
,
i
=
1
,
…
,
2
m
,
N
S
0
0
I
T
A
s
i
T
S
+
S
A
s
i
S
0
B
s
0
C
s
T
·
-
γ
I
0
I
0
0
·
·
-
γ
I
N
S
0
0
I
<
0
,
i
=
1
,
…
,
2
m
,
R
I
I
S
≥
0
,where NR and NS denote bases of the null spaces of BsTI0 and CsI0, respectively.The proof of this theorem and additional details can be found in [19]. Conditions (6) are numerically tractable and solvable. From these sufficient conditions an H∞ gain-scheduled controller can be synthesized.In Theorem1, the solvability conditions are defined by two positive definite matrices R>0 and S>0. If there exist feasible solutions, the gain-scheduled controller matrices in form (7)
K
∞
Φ
=
A
K
Φ
B
K
Φ
C
K
Φ
D
K
Φ
≔
∑
i
2
m
λ
i
A
K
i
B
K
i
C
K
i
D
K
iare then calculated following the method described in [23]. The main steps to the understanding of the gain-scheduling control design will be described. Firstly, consider the closed loop system (8)
x
˙
x
˙
K
z
1
z
2
=
A
s
+
B
s
D
K
C
s
B
s
C
K
B
s
D
K
B
s
B
K
C
s
A
K
B
K
0
D
K
C
s
C
K
D
K
0
C
s
0
I
0
x
x
K
w
1
w
2which can be written by the state-space equations (9)Σ0≔x˙cl=AclΦxcl+BclΦwz=CclΦxcl+DclΦwwith xK being the controller state, xcl=xTxKTT, w=w1Tw2TT, and z=z1Tz2TT. Here, Acl, Bcl, Ccl, and Dcl are the affine functions of K∞Φ. In a second step, find the Lyapunov matrix P from of the following linear equation: (10)PRIMT0=IS0NT.P is a unique solution, where M and N are full row rank matrices with MNT=I-RS [23]. Now, using the well knownBounded Real Lemma [24] that ensures the internal stability and the H∞ norm constraint, determine the gain-scheduled controller if and only if there exists a P>0 such that (11)AclTξiP+PAclξiPBclξiCclTξi·-γIDclTξi··-γI<0,i=1,…,2m.Applying the procedure described in [19, 20, 23] for the LMIs conditions above, we obtain the gain-scheduled H∞ loop shaping controller that ensures the robust stability of the closed loop system subject to constraint of input saturation nonlinearity. Now, some important results that are required to establish the main results of this paper will be described.


## 3. Main Results

Consider the plantG described by state-space models in the form (12)GΔ≔x˙t=A+ΔAxt+Bvtyt=Cxt+Dvt,where x∈Rn is the state vector, v∈Rm the input vector, and y∈Rp the output vector. Herein, A, B, C, and D are known constant matrices that describe the nominal system and ΔA is a matrix function representing the time-varying parametric uncertainty. The parametric uncertainty is considered in form ΔA=FΔtE, where F and E are known constant matrices with appropriate dimensions and Δt is an unknown matrix with Lebesgue measurable elements such that Δt2≤1 [25]. Now, consider that the dynamic weighting function W can be defined as (13)W≔x˙wt=Awxwt+Bwptut=Cwxwt+Dwpt,where xw∈Rnw is the state vector, p∈Rmw the input vector, and u∈Rpw the output vector of the dynamic weighting function. Following the same procedure described by [19] the state-space model of the H∞ loop shaping framework (Figure 1) in the presence of input saturation and uncertainties in the A matrix can be written as (14)x˙t=A+ΔAxt+BΘCwxwt+BΘDwpt,x˙wt=Awxwt+Bwpt,yt=Cxt+DΘCwxw+DΘDwpt.Following a few mathematical manipulations we can write the shaped plantGsΔΦ as (15)
x
˙
x
˙
w
y
=
A
+
Δ
A
B
Θ
C
w
B
Θ
D
w
0
A
w
B
w
C
D
Θ
C
w
D
Θ
D
w
x
x
w
p
,
G
s
Δ
Φ
⟹
x
˙
s
y
=
A
s
+
Δ
A
s
︷
A
D
s
B
s
C
s
D
s
x
˙
s
p
.Thus, the state-space matrices can be written in the form(16)ADs=ABΘCw0Aw︸As+FΔtE000︸ΔAs;As=ABCw0Aw+B0I-Θ0-Cw;Bs=BDwBw+B0I-Θ-Dw;Cs=CDCw+DI-Θ0-Cw;Ds=DΘDw=DDw+DI-Θ-Dw.MakingΦ=I-Θ and substituting (16) and emphasizing that Φ=diagϕ1,…,ϕm with 0≤ϕj≤1-αj we obtain (17)As=A~+B-ΦC-,Bs=B~+B-ΦD-,Cs=C~+DΦC-,Ds=D~+DΦD-.For technical simplification, it is considered that G and W are strictly proper; that is, D=0 and Dw=0 in (16) such that overall closed loop system is given by (18)Σ1≔x˙cl=AclΔΦxcl+BclΦwz=CclΦxcl+Dclw,where xcl=xTxKTT and (19)AclΔΦ=AclΦ+F0︸F~ΔE0︸E~withAclΦ=AsBsCKBKCsAK;BclΦ=0BsBK0;CclΦ=0CKCs0;Dcl=00I0.This system is quadratically stable with Tzw∞≤γ [26]. From the generalized plant in four-block configuration considering DKΦ=0, synthesis conditions are now presented for the design of the gain-scheduled parametric H∞ loop shaping controller subject to both constraint of input saturation and parametric uncertainties.Theorem 2.
There exists a stabilizing gain-scheduled controllerK∞Φ subject to both constraint of input saturation with -umax,j≤uj≤umax,j and parametric uncertainties such that γ≥1 if for some μ>0 there exist Y>0, X>0, satisfying the following inequalities: (20)Ξ1Ξ2Ξ3Ξ4·-γIDclT0··-γI0···-μI<0fori=1,…,2m,YIIX>0,where (21)Ξ1=AsiY+BsHi+YAsiT+HiTBsT⋯⋯Asi+MiTMi+AsiT⋯⋯XAsi+CsTLiT+LiCs+AsiTX,Ξ2=0BsLiXBs,Ξ3=HiTYCsT0CsT,Ξ4=YETμFETμXF.Moreover, a suitable gain-scheduled controller when Φ varies in the polytope is described as (22)CKi=HiV,BKi=UT-1Li,AKi=UT-1Mi-XAsiY-XBsHi-UTBKiCsYV-1,where U and V are arbitrary nonsingular matrices satisfying VUT=I-XY with i=1,…,2m.Proof.
According to [21], a closed loop system Acli,Bcli,Ccli,Dcli is stable with Tzw∞≤γ if and only if there exists a P>0 such that (23)AcliTP+PAcliPBcliCcliT·-γIDcliT··-γI<0,i=1,…,2m.
For norm-bounded parametric uncertainty, an important lemma will be used to prove the main results in this paper.
Lemma 3 (see [27]).ConsiderQ=QT,GandWmatrices with appropriate dimensions(24)Q+GΔtW+WTΔtTGT<0forΔt2≤1if only if there existsμ>0such that(25)Q+μGGT+μ-1WTW<0.
The lemma above and Schur complement imply that there exists a Lyapunov matrixP=PT>0 such that (26)AcliTP+PAcliPBcliCcliTE~TμPF~·-γIDclT00··-γI00···-μI0····-μI<0,where (27)Q=AcliTP+PAcliPBcliCcliT·-γIDcl··-γI,G=PF00,W=E00.
Now, partition the Lyapunov matrixP in accordance with the structure of the matrix Acl and matrices given by X, U, Y, and V [24]; we obtain (28)P=XUTUX^,P-1=YVTVY^with X=XT, Y=YT, and VUT=I-XY. To eliminate the nonlinear terms in (26) it is necessary to consider the transformation matrix Ψ given by (29)Ψ=YIV0.
Thus it is possible to apply the following congruence transformation onP for the inequality P>0: (30)P>0⟹ΨTPΨ>0,YVTI0XUTUX^YIV0=YIIX>0.Now, premultiplying and postmultiplying (26) by diag⁡ΨTIIII and its transpose, respectively, yield(31)Γ1ΨTPBcliΨTCcliTΨTE~TμΨTPF~·-γIDclT00··-γI00···-μI0····-μI<0with Γ1=ΨTAcliTP+PAcliΨ. Solving the terms CcliΨ, BcliTPΨ, and Γ1 we obtain a gain-scheduled controller K∞Φ that ensures the robust stability of the closed loop system subject to the input saturation constraint and norm-bounded parametric uncertainty: (32)CKi=HiV,BKi=UT-1Li,AKi=UT-1Mi-XAsiY-XBsHi-UTBKiCsYV-1,where the optimization variables are γ, X, Y, Hi, Li, and Mi with VUT=I-XY and i=1,…,2m.It can be noted that (20) cannot be solved directly via an LMI software, due to the product of μ with X. To overcome this difficulty, it is important to set an a priori value for μ and afterward use an LMI solver. Thus it is necessary to tune the parameter μ until a feasible solution is found.


## 4. Numerical Example

This section describes a realistic design of a gain scheduledH∞ loop shaping controller considering both input saturation constraint and parametric uncertainties for a VTOL helicopter. The synthesis procedure described in this section was implemented using the following software suite: Matlab 7.5.0, SeDuMi [28], and Yalmip [29].


### 4.1. VTOL Helicopter

This example was inspired by [22] where a robust controller was designed in order to keep the closed loop system stable when subject to airspeed changes. Here, the model is slightly modified to address the present problem, being described by the following states equations: (33)GΔ≔x˙t=A+ΔAxt+Bvtyt=Cxt+Dvtwith (34)A=-0.03660.02710.0188-0.45550.0482-1.0100.0024-4.02080.1002ρ1-0.707ρ20010;B=0.44220.17613.5446-7.592-5.524.4900;C=0100;D=0,where the states variables are horizontal velocity, x1, vertical velocity, x2, pitch rate, x3, and pitch angle, x4; the saturating input variables are collective pitch control, v1, and longitudinal cyclic pitch control, v2. Moreover, the following bound parameters are assumed: ρ1=0.3681+Δρ1 with Δρ1≤0.05 and ρ2=1.42+Δρ2 with Δρ2≤0.01. Now, assuming the parametric uncertainty ΔA=FΔtE, we can represent the uncertainty using the following matrices: (35)F=00000.050.0100,E=01000001forΔt2≤1.The following specifications are chosen for the controller to be designed:(1) reference signal tracking and disturbance rejection with error not exceeding 10% (2) zero steady error for the step input; (3) stability of the controlled system for input saturation limits of umax/min= ±2.5. Typically, a dynamic weighting function W is used to enforce the performance specifications in the frequency domain. This weighting function W must be selected in order to obtain high gain at low frequencies, roll-off rates of approximately 20 dB/decade at the desired bandwidth(s), and higher rates at high frequencies [30]. To satisfy these requirements, we chose(36)W=12.5s+70.01s2+4s+0.80012.5s+20.01s2+2s.From this choice the shaped plant has been defined. The frequency response of the shaped plant in comparison to nominal plant is presented in Figure2.Figure 2
Singular values of the shaped and nominal plants.To demonstrate the effectiveness of the proposed method, two different designs will be discussed. The first consists in the design of anH∞ loop shaping controller for the VTOL helicopter considering only norm-bounded parametric uncertainty; this design will be used as a reference for performance evaluation. The second consists in the proposed LPV approach considering both the input saturation constraint and norm-bounded parametric uncertainty. In this design, the same dynamic weighting function and μ=52000 have been considered. Suitable values of μ can be found by a grid search, in this example in the interval (0,105]. Furthermore, two varying parameters ϕ1,ϕ2 that indicate the level of actuator’s saturation are defined. Herein, it is assumed that ϕ1 varies in the range between 0 and 0.4 and ϕ2 varies between 0 and 0.2, representing a polytope with four vertices and input saturation limits around umax/min= ±2.5. For both designs the maximum robust stability margin εmax>0.25 (γ<4) was achieved. In the absence of input saturation, that is, considering only norm-bounded parametric uncertainty, the H∞ loop shaping controller was a robust stability margin of 0.60 while the gain-scheduled H∞ controller was a margin of 0.40.Figures3 and 4 illustrate the performance of the two controllers designed. They satisfied the requirements of reference signal tracking, zero steady error for the step input, and guarantee of robust stability of the controlled system for input saturation of umax/min= ±2.5. It can be noted that another simulation for better evaluation of the proposed method is incorporated. It consists basically in the performance of the H∞ loop shaping controller designed considering a saturation block. Taking into account the control input in both channels obtained with the two controllers designed, it is observed that gain scheduled H∞ loop shaping has a better performance than the H∞ loop shaping controller incorporating the saturation block.Figure 3
Step response for the controllers designed considering input saturation and parametric uncertainty.Figure 4
Control input in both channels for the controllers designed considering input saturation and parametric uncertainty.Next, the guaranteed simultaneous gain/phase margins [31, 32] were used to evaluate the controlled system’s robustness. These margins were defined as (37)-20log10⁡1+ε1-εdB≤GM≤20log10⁡1+ε1-εdB-sin-1⁡εdegrees≤PM≤sin-1⁡εdegrees.TheH∞ loop shaping controller considering only norm-bounded parametric uncertainty has a gain margin of ±6.02 dB; its phase margin is 36.86∘. The gain-scheduled H∞ loop shaping controller has a gain margin of ±3.67 dB and phase margin of 23.57∘. The guaranteed simultaneous gain/phase margins for the gain-scheduled controller are less than for the LTI controller without input saturation considering only parametric uncertainties. This conservative margin occurs because in (22) there is a style set of X, Y being used for all polytope vertices. From these results, it is concluded that the proposed method is an interesting alternative for systems subject to input saturation and parametric uncertainties.


## 4.1. VTOL Helicopter

This example was inspired by [22] where a robust controller was designed in order to keep the closed loop system stable when subject to airspeed changes. Here, the model is slightly modified to address the present problem, being described by the following states equations: (33)GΔ≔x˙t=A+ΔAxt+Bvtyt=Cxt+Dvtwith (34)A=-0.03660.02710.0188-0.45550.0482-1.0100.0024-4.02080.1002ρ1-0.707ρ20010;B=0.44220.17613.5446-7.592-5.524.4900;C=0100;D=0,where the states variables are horizontal velocity, x1, vertical velocity, x2, pitch rate, x3, and pitch angle, x4; the saturating input variables are collective pitch control, v1, and longitudinal cyclic pitch control, v2. Moreover, the following bound parameters are assumed: ρ1=0.3681+Δρ1 with Δρ1≤0.05 and ρ2=1.42+Δρ2 with Δρ2≤0.01. Now, assuming the parametric uncertainty ΔA=FΔtE, we can represent the uncertainty using the following matrices: (35)F=00000.050.0100,E=01000001forΔt2≤1.The following specifications are chosen for the controller to be designed:(1) reference signal tracking and disturbance rejection with error not exceeding 10% (2) zero steady error for the step input; (3) stability of the controlled system for input saturation limits of umax/min= ±2.5. Typically, a dynamic weighting function W is used to enforce the performance specifications in the frequency domain. This weighting function W must be selected in order to obtain high gain at low frequencies, roll-off rates of approximately 20 dB/decade at the desired bandwidth(s), and higher rates at high frequencies [30]. To satisfy these requirements, we chose(36)W=12.5s+70.01s2+4s+0.80012.5s+20.01s2+2s.From this choice the shaped plant has been defined. The frequency response of the shaped plant in comparison to nominal plant is presented in Figure2.Figure 2
Singular values of the shaped and nominal plants.To demonstrate the effectiveness of the proposed method, two different designs will be discussed. The first consists in the design of anH∞ loop shaping controller for the VTOL helicopter considering only norm-bounded parametric uncertainty; this design will be used as a reference for performance evaluation. The second consists in the proposed LPV approach considering both the input saturation constraint and norm-bounded parametric uncertainty. In this design, the same dynamic weighting function and μ=52000 have been considered. Suitable values of μ can be found by a grid search, in this example in the interval (0,105]. Furthermore, two varying parameters ϕ1,ϕ2 that indicate the level of actuator’s saturation are defined. Herein, it is assumed that ϕ1 varies in the range between 0 and 0.4 and ϕ2 varies between 0 and 0.2, representing a polytope with four vertices and input saturation limits around umax/min= ±2.5. For both designs the maximum robust stability margin εmax>0.25 (γ<4) was achieved. In the absence of input saturation, that is, considering only norm-bounded parametric uncertainty, the H∞ loop shaping controller was a robust stability margin of 0.60 while the gain-scheduled H∞ controller was a margin of 0.40.Figures3 and 4 illustrate the performance of the two controllers designed. They satisfied the requirements of reference signal tracking, zero steady error for the step input, and guarantee of robust stability of the controlled system for input saturation of umax/min= ±2.5. It can be noted that another simulation for better evaluation of the proposed method is incorporated. It consists basically in the performance of the H∞ loop shaping controller designed considering a saturation block. Taking into account the control input in both channels obtained with the two controllers designed, it is observed that gain scheduled H∞ loop shaping has a better performance than the H∞ loop shaping controller incorporating the saturation block.Figure 3
Step response for the controllers designed considering input saturation and parametric uncertainty.Figure 4
Control input in both channels for the controllers designed considering input saturation and parametric uncertainty.Next, the guaranteed simultaneous gain/phase margins [31, 32] were used to evaluate the controlled system’s robustness. These margins were defined as (37)-20log10⁡1+ε1-εdB≤GM≤20log10⁡1+ε1-εdB-sin-1⁡εdegrees≤PM≤sin-1⁡εdegrees.TheH∞ loop shaping controller considering only norm-bounded parametric uncertainty has a gain margin of ±6.02 dB; its phase margin is 36.86∘. The gain-scheduled H∞ loop shaping controller has a gain margin of ±3.67 dB and phase margin of 23.57∘. The guaranteed simultaneous gain/phase margins for the gain-scheduled controller are less than for the LTI controller without input saturation considering only parametric uncertainties. This conservative margin occurs because in (22) there is a style set of X, Y being used for all polytope vertices. From these results, it is concluded that the proposed method is an interesting alternative for systems subject to input saturation and parametric uncertainties.


## 5. Conclusions

New sufficient LMI conditions for the synthesis of gain scheduledH∞ loop shaping controllers considering both input saturation constraints and norm-bounded parametric have been presented. The methodology addresses the design problem in the four-block H∞ synthesis framework, in which it is possible, through a linear parameter-varying structure, to describe the parametric uncertainty and the input saturation nonlinearity as perturbations to normalized coprime factors of the shaped plant. Moreover, it is observed that the synthesis is quite simple, but in practical terms some adjustments are necessary to avoid high gains and an inappropriate limited control input must be considered. One difficulty found in this methodology is the exhaustive attempt to find the parameter μ, which is capable of providing good performance and a feasible solution. Future work should use computational techniques in order to reduce this exhaustive attempt to find μ. Finally, performance and robustness analysis illustrate that the controllers obtained can be an advantageous strategy for a class of linear systems with the presence of both constraint of input saturation and parametric uncertainty.



---

*Source: 383297-2015-07-06.xml*