# Adaptive Terminal Sliding Mode NDO-Based Control of Underactuated AUV in Vertical Plane

Authors: Wei Chen; Yanhui Wei; Jianhui Zeng; Han Han; Xianqiang Jia
Journal: Discrete Dynamics in Nature and Society
 (2016)

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## Abstract

The depth tracking issue of underactuated autonomous underwater vehicle (AUV) in vertical plane is addressed in this paper. Considering the complicated dynamics and kinematics model for underactuated AUV, a more simplified model is obtained based on assumptions. Then a nonlinear disturbance observer (NDO) is presented to estimate the external disturbance acting on AUV, and an adaptive terminal sliding mode control (ATSMC) based on NDO is applied to enhance the depth tracking performance of underactuated AUV considering both internal and external disturbance. Compared with the traditional sliding mode controller, the static error and chattering problem of the depth tracking process have been clearly improved by adopting NDO-based ATSMC. The stability of control system is proven to be guaranteed according to Lyapunov theory. In the end, simulation results imply that the proposed controller owns strong robustness and satisfied control effectiveness in comparison with the traditional controller.


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## Body

## 1. Introduction

Autonomous underwater vehicle (AUV) is a kind of underwater vehicles with onboard power supply and intelligence. Due to its autonomous ability, AUV has become a very hot research topic in the past few decades for its wide applications in ocean scientific survey, emergency rescue, and military mission, such as gas and mine sources detection, marine environment research, and observation and manipulation task of military [1–3]. Traditionally, fully actuated AUVs are applied to underwater missions in the ocean. The trait of fully actuated AUVs is that the number of actuators equals degrees of freedom (DOF). However, with the development of ocean equipment, underactuated AUVs play more and more important role in marine tasks, whose degrees of freedom (DOF) are more than actual control inputs. In practical occasions, many AUVs are actually underactuated as they lack thrusters or other actuators in the heave or sway directions, and when the actuator failures of fully actuated AUVs occur, they will also transform into underactuated states. A significant advantage for underactuated AUV is that its own weight can be decreased due to less actuators and sensors. However, these underactuated AUVs bring a challenging control issue considering their lack of actuators. Furthermore, they own highly coupled and strongly nonlinear kinematic and dynamic models [4]. Therefore, various controllers for underactuated AUV need to estimate and deal with uncertainties, hydrodynamic effects, inherent nonlinearity, and so on.Trajectory tracking in the vertical plane is one of the essential research problems for underactuated AUVs; many researchers have concentrated upon this control problem. Subudhi et al. [2] proposed an output feedback controller to complete AUV’s path tracking task in its vertical plane according to a linearized motion model. Cristi et al. [5] applied sliding mode approach to overcome dynamical uncertainties of AUV in a diving maneuver; Lapierre [6] presented a robust diving control technique based on adaptive backstepping technique and switching schemes, which could meet the robustness requirement; Naik and Singh [7] treated the suboptimal control issue of underactuated AUV by using SDRE technique, and AUV showed satisfactory performance in spite of system’s uncertainties and other constraints. Loc et al. [8] used an analytical method based on sliding mode control technique to obtain the optimal trajectory for unmanned underwater vehicle, which made the vehicle follow the desired trajectory as well. Adhami-Mirhosseini et al. [9] put forward a controller combining Fourier series and pseudo-spectral thoughts to solve the bottom tracking problems for AUV. Lakhekar et al. constructed an enhanced dynamic fuzzy SMC to improve the control performance of AUV’s depth following mission [10].Sliding mode control (SMC) is noticed for its strong robustness against various disturbances and uncertainties in the application of controllers [11]. Due to this, SMC has been widely used by many researchers in different systems [12–14], such as the inverted-pendulum control systems, and motion control systems for underwater vehicles. Jia et al. [15] built an iterative nonlinear sliding mode (INSM) controller to achieve bottom-following of underactuated AUV; Lakhekar and Saundarmal [16] introduced a novel fuzzy sliding mode controller for AUV’s depth control task. SMC can handle system’s uncertainties in a robust way. However, SMC is limited for its dependence on the plant model. To overcome this disadvantage, lots of controllers based on adaptive thoughts are introduced. Adaptive control algorithm is an efficient way to enhance the performance of AUV’s tracking controllers [17]. Antonelli et al. [18] proposed an adaptive controller for ODIN AUV; Yu et al. [19] used adaptive technique on the basis of neural networks to control AUV; Zhang et al. [20] constructed an adaptive output feedback controller combined with DRFNN method to realize the tracking control target of AUV. Disturbance is another key factor which affects AUV, and it is always unpredictable, nonperiodic, and even highly nonlinear. Nonlinear disturbance observer (NDO) [21] is an effective method to reduce the bad influence of unknown external disturbance on the plant. Accounting for this, NDO has been widely applied into estimating and compensating disturbances in many areas, such as manipulators and motors [22, 23]. The application of NDO in underactuated AUV is crucial to handle with the unexpected condition in presence of disturbance.In this paper, an adaptive sliding mode controller based on NDO is introduced to solve the depth tracking problems of AUV. Firstly, the mathematical model of underactuated AUV in the vertical plane is described and simplified. Secondly, a NDO is built with reference to the above AUV’s model, and an adaptive terminal sliding mode control (ATSMC) method considering NDO is presented as well. The system’s stability is proven by choosing appropriate Lyapunov functions in the controller’s construction process. Finally, the simulation experiments verify the effectiveness of the proposed controller, and the robustness of underactuated AUV’s depth tracking in the vertical plane is shown in the case of external disturbance.


## 2. Mathematical Model of Underactuated AUV

The frame coordinate systems’ schematic drawing for the mathematical model of underactuated AUV is shown in Figure1; the reference frame oE-xEyEzE is fixed on the earth, wherein ϕ, θ, and φ are the roll angle, pitch angle, and yaw angle, respectively, in this reference frame; the body-fixesd reference frame oB-xByBzB is fixed on the underactuated AUV; its origin oB is located at AUV’s center of buoyancy, wherein u,v,w,p,q, and r are the surge velocity, sway velocity, heave velocity, roll angular velocity, pitch angular velocity, and yaw angular velocity, respectively, in the body-fixed reference frame.Figure 1
Coordinate systems of underactuated AUV.As underactuated AUV is a highly nonlinear and coupled system, its kinematics and dynamics equations in the vertical plane are constituted of a few nonlinear equations, which can be expressed in the following forms [7]: (1)z˙=wcos⁡θ-usin⁡θ,m-Zw˙w˙=Zq˙q˙+muq+xGq˙+zGq2+Zuquq+Zuwuw+Zwwww+Zqqqq+Weig-Buocos⁡θ+Zuuu2δs,θ˙=q,Iyy-Mq˙q˙=mxWw˙-uq-zGwq+Mw˙w˙+Muquq+Muwuw+Mwwww+Mqqqq-xWWeig-xBBuocos⁡θ-zWWeig-zBBuosin⁡θ+Muuu2δs+Dout,where Iyy is the inertial movement of AUV about the pitch axis yB, m is the mass of AUV, (xW,yW,zW), (xB,yB,zB) are the coordinates of AUV’s mass center and buoyancy center, respectively, in the body-fixed frame oB-xByBzB, z is the diving depth of AUV in the frame oE-xEyEzE, Weig is the weight of AUV, Buo is the buoyancy of AUV in the water, Dout is the disturbance from the outside environment, δs is the rudder input, Muu,Zuu are the coefficients of rudder, and Zw˙,Zq˙,Zuq,Zuw,Zww,Zqq,Mq˙,Mw˙,Muq,Muw,Mww,Mqq are the hydrodynamics coefficients of AUV.Aiming at simplifying the controller design for underactuated AUV, the effect of heave velocityw is neglected by assuming w≈0,w˙≈0; according to the definition of body-fixed frame oB-xByBzB, it is obvious that xB=yB=zB=0. It is assumed that the surge velocity u keeps a constant value as u=Uc,Uc>0 with the help of main thruster mechanism. Define x1=z,x2=θ,and x3=q; the previous equations would be replaced by a new one:(2)x˙1=-Ucx2+D1,x˙2=x3,x˙3=M1x3+M2sin⁡x2+M3δs+M4x3x3+M5cos⁡x2+D2,where M1=MuqUc/Iyy-Mq˙,  M2=-zWWeig/Iyy-Mq˙,M3=MuuUc2/Iyy-Mq˙,M4=Mqq/Iyy-Mq˙, M5=-xWWeig/Iyy-Mq˙,D1=Uc(x2-sinx2), and D1 is regarded as internal disturbance of state equations,  D2=Dout/Iyy-Mq˙, and D2 is regarded as external disturbance of state equations, which is also the main disturbance for the system.


## 3. Terminal Sliding Mode Control Based on NDO

### 3.1. Nonlinear Disturbance Observer Design

The main disturbance for the underactuated AUV is external disturbanceDout; for the purpose of eliminating its effect on the motion of AUV, it is necessary to predict the Dout changing tend. This thought is transformed into estimating D2 in the progress of constructing controller. Assume D^2 is the estimation value of D2, D^out is the estimation value of Dout, and, obviously, D^out=(Iyy-Mq˙)D^2. Define state variable vector x=[x1,x2,x3]T; according to (2), a vector-form state equation is obtained [24, 25]:(3)x˙=Gx+Exδs+JxD2,where(4)Gx=-Ucx2+D1x3M1x1+M2sin⁡x2+M4x3x3+M5cos⁡x2;Ex=00M3;Jx=001.Definef is the internal state variable of NDO; then a nonlinear disturbance observer can be constructed as below:(5)f=D^2-hx,f˙=-TGx+Exδs+Jxf+hx,where h(x) can be designed as h(x)=h1x1+h2x2+h3x3 under the condition that h1>0,h2>0,h3>0,T=[h1,h2,h3].DefineD~2=D2-D^2. In fact, there is seldom or even no prior knowledge about the changing law of D2. Only when the changing speed of D2 is slower compared with whole system’s dynamic traits, it is preferable to assume the derivative of D2 satisfies D˙2=0.From (5), we can obtain (6)D~˙2=D˙2-D^˙2=-D^˙2,D^˙2=f˙+h˙x=TJxD~2.ThenD~˙2 can be gotten as(7)D~˙2=-TJxD~2=-h3D~2.From (7), the value of D~2 converges to zero due to the fact that h3>0.


### 3.2. Adaptive Terminal Sliding Mode Control

The overall schematic drawing for the NDO-based terminal sliding mode control method is presented in Figure2. Considering D^2 is available, (2) can be rewritten as (8)x˙1=-Ucx2+D1,x˙2=x3,x˙3=M1x3+M2sin⁡x2+M3δs+M4x3x3+M5cos⁡x2+D^2+Ψ,where Ψ=D~2.Figure 2
The structure of NDO-based terminal sliding mode control for AUV.Let us define the estimation ofΨ is Ψ^, and Ψ~=Ψ-Ψ^. According to the same principle of getting the derivate law of D~2, assuming Ψ varies more slowly compared to the system dynamics, then we have Ψ˙=0, and Ψ~˙=Ψ˙-Ψ^˙=-Ψ^˙ in further step.The following task is to design sliding surfaces for this controller construction based on the basic idea of backstepping strategy. In order to follow the desired depth trajectory, the first sliding surface is defined as(9)S1=z-zd=x1-zd.Considering thatD1 is another equivalent internal disturbance in the state equations, it is necessary to estimate its value as well. Define D^1 as the estimation of D1, and D~1=D1-D^1. D^1 can be obtained by introducing the following adaptive control law, as shown in (10). Consider (10)D^˙1=ε1S1,ε1>0.Aiming at eliminating the static error of depth tracking, an integral part relative toS1 is necessarily included:(11)Ω1=∫0tS1dt.Choose a Lyapunov function as(12)V1=12S12+12ε1D~12+12KΩ1Ω12,KΩ1>0.Define a virtual input as(13)α1=1Uck1S1+D^1+KΩ1Ω1-z˙d,k1>0.Define the second sliding surface and choose a Lyapunov function separately as(14)S2=θ-α1=x2-α1,V2=V1+12S22.Define a virtual input and the tracking error of pitch angular velocity separately as(15)α2=-k2S2+α˙1+Uce1,k2>0,(16)S3=q-α2=x3-α2.Then a novel terminal sliding mode surface is constructed as(17)σ=S3+∫0tm1sigr1S3+m2sigr2S3dτ,where sigr1S3=S3r1sign⁡S3,sigr2S3=S3r2sign⁡(S3), m1,m2,r1≥1,0<r2<1,  all of them are positive constant, and the definition of sign⁡S3 is sign⁡S3=1,S3>0; 0,S3=0; -1,S3<0.In order to obtain the change rule ofΨ^, the adaptive law of Ψ^ is built as(18)Ψ^˙=ε2σ,ε2>0.According to above adaptive laws and sliding mode surfaces design process, the control input of rudder is finally established as below:(19)δs=-1M3M1x3+M2sin⁡x2+M4x3x3+M5cos⁡x2+Ψ^+D^2-α˙2+m1sigr1S3+m2sigr2S3+LweightWσsign⁡σ,Lweight>0,where Wσ=∫0t(LWWσ+Kσσ)dt,LW<0,Kσ>0, and this weighted integral section Wσ is designed to reduce the chattering phenomenon of traditional sliding mode control.Define a Lyapunov function which includes the NDO part and terminal sliding surface as(20)V3=12σ2+12D~22+12ε2Ψ~2.


### 3.3. Stability Analysis of System

To study the convergence of depth trajectory, each sliding mode surface of controller should be analyzed. These positive definite Lyapunov functions constructed asVi(i=1,2,3) are discussed in detail to study sliding mode surfaces’ convergent issues, and their derivatives with respect to time can be obtained as(21)V˙1=S1S˙1+1ε1D~1D~˙1+KΩ1Ω1Ω˙1=S1-Ucx2+D1-z˙d-1ε1D~1D^˙1+KΩ1Ω1S1=S1-UcS2-Ucα1+D1-z˙d-1ε1D~1D^˙1+KΩ1Ω1S1=S1-UcS2-k1S1+D~1-KΩ1Ω1-1ε1D~1D^˙1+KΩ1Ω1S1=-k1S12-UcS1S2.IfS2=0, we can conclude that V1=-k1S12≤0, which means S1 gradually converges to zero. Then the next step is to study(22)V˙2=V˙1+S2S˙2=-k1S12-UcS1S2+S2S3+α2-α˙1=-k1S12-UcS1S2+S2S3-k2S2+α˙1+UcS1-α˙1=-k1S12-k2S22+S2S3.In the same way, ifS3=0, then we can conclude that V2=-k1S12-k2S22≤0, which means S2 gradually converges to zero as well. From (17), it is easy to know that S3=0 is equivalent to σ=0; the key point is to verify the convergence of terminal sliding mode surface σ.From (8), (13), (15), (17), and (19), we can obtain (23)σ˙=S˙3+m1sigr1S3+m2sigr2S3=x˙3-α˙2+m1sigr1S3+m2sigr2S3=M1x3+M2sin⁡x2+M3δs+M4x3x3+M5cos⁡x2+D^2+Ψ-α˙2+m1sigr1S3+m2sigr2S3=-LweightWσsign⁡σ+Ψ~.From (7), (18), and (23), we can obtain(24)V˙3=σσ˙+D~2D~˙2+1ε2Ψ~Ψ~˙=σ-LweightWσsign⁡σ+Ψ~-h3D~22-1ε2Ψ~Ψ^˙=-LweightWσσ-h3D~22≤0.Considering the fact thatV3≥0,V˙3≤0, it is easy to know that terminal sliding mode surface σ is reachable. Once σ=0, we can know that S3 will converge rapidly to zero in a finite period. Due to (22), S2 converges to zero as well when S3=0; and once S2=0, it implies that S1 converges to zero quickly. All above analysis proves that all closed-loop sliding mode surfaces will converge to the equilibrium points in finite time.


## 3.1. Nonlinear Disturbance Observer Design

The main disturbance for the underactuated AUV is external disturbanceDout; for the purpose of eliminating its effect on the motion of AUV, it is necessary to predict the Dout changing tend. This thought is transformed into estimating D2 in the progress of constructing controller. Assume D^2 is the estimation value of D2, D^out is the estimation value of Dout, and, obviously, D^out=(Iyy-Mq˙)D^2. Define state variable vector x=[x1,x2,x3]T; according to (2), a vector-form state equation is obtained [24, 25]:(3)x˙=Gx+Exδs+JxD2,where(4)Gx=-Ucx2+D1x3M1x1+M2sin⁡x2+M4x3x3+M5cos⁡x2;Ex=00M3;Jx=001.Definef is the internal state variable of NDO; then a nonlinear disturbance observer can be constructed as below:(5)f=D^2-hx,f˙=-TGx+Exδs+Jxf+hx,where h(x) can be designed as h(x)=h1x1+h2x2+h3x3 under the condition that h1>0,h2>0,h3>0,T=[h1,h2,h3].DefineD~2=D2-D^2. In fact, there is seldom or even no prior knowledge about the changing law of D2. Only when the changing speed of D2 is slower compared with whole system’s dynamic traits, it is preferable to assume the derivative of D2 satisfies D˙2=0.From (5), we can obtain (6)D~˙2=D˙2-D^˙2=-D^˙2,D^˙2=f˙+h˙x=TJxD~2.ThenD~˙2 can be gotten as(7)D~˙2=-TJxD~2=-h3D~2.From (7), the value of D~2 converges to zero due to the fact that h3>0.


## 3.2. Adaptive Terminal Sliding Mode Control

The overall schematic drawing for the NDO-based terminal sliding mode control method is presented in Figure2. Considering D^2 is available, (2) can be rewritten as (8)x˙1=-Ucx2+D1,x˙2=x3,x˙3=M1x3+M2sin⁡x2+M3δs+M4x3x3+M5cos⁡x2+D^2+Ψ,where Ψ=D~2.Figure 2
The structure of NDO-based terminal sliding mode control for AUV.Let us define the estimation ofΨ is Ψ^, and Ψ~=Ψ-Ψ^. According to the same principle of getting the derivate law of D~2, assuming Ψ varies more slowly compared to the system dynamics, then we have Ψ˙=0, and Ψ~˙=Ψ˙-Ψ^˙=-Ψ^˙ in further step.The following task is to design sliding surfaces for this controller construction based on the basic idea of backstepping strategy. In order to follow the desired depth trajectory, the first sliding surface is defined as(9)S1=z-zd=x1-zd.Considering thatD1 is another equivalent internal disturbance in the state equations, it is necessary to estimate its value as well. Define D^1 as the estimation of D1, and D~1=D1-D^1. D^1 can be obtained by introducing the following adaptive control law, as shown in (10). Consider (10)D^˙1=ε1S1,ε1>0.Aiming at eliminating the static error of depth tracking, an integral part relative toS1 is necessarily included:(11)Ω1=∫0tS1dt.Choose a Lyapunov function as(12)V1=12S12+12ε1D~12+12KΩ1Ω12,KΩ1>0.Define a virtual input as(13)α1=1Uck1S1+D^1+KΩ1Ω1-z˙d,k1>0.Define the second sliding surface and choose a Lyapunov function separately as(14)S2=θ-α1=x2-α1,V2=V1+12S22.Define a virtual input and the tracking error of pitch angular velocity separately as(15)α2=-k2S2+α˙1+Uce1,k2>0,(16)S3=q-α2=x3-α2.Then a novel terminal sliding mode surface is constructed as(17)σ=S3+∫0tm1sigr1S3+m2sigr2S3dτ,where sigr1S3=S3r1sign⁡S3,sigr2S3=S3r2sign⁡(S3), m1,m2,r1≥1,0<r2<1,  all of them are positive constant, and the definition of sign⁡S3 is sign⁡S3=1,S3>0; 0,S3=0; -1,S3<0.In order to obtain the change rule ofΨ^, the adaptive law of Ψ^ is built as(18)Ψ^˙=ε2σ,ε2>0.According to above adaptive laws and sliding mode surfaces design process, the control input of rudder is finally established as below:(19)δs=-1M3M1x3+M2sin⁡x2+M4x3x3+M5cos⁡x2+Ψ^+D^2-α˙2+m1sigr1S3+m2sigr2S3+LweightWσsign⁡σ,Lweight>0,where Wσ=∫0t(LWWσ+Kσσ)dt,LW<0,Kσ>0, and this weighted integral section Wσ is designed to reduce the chattering phenomenon of traditional sliding mode control.Define a Lyapunov function which includes the NDO part and terminal sliding surface as(20)V3=12σ2+12D~22+12ε2Ψ~2.


## 3.3. Stability Analysis of System

To study the convergence of depth trajectory, each sliding mode surface of controller should be analyzed. These positive definite Lyapunov functions constructed asVi(i=1,2,3) are discussed in detail to study sliding mode surfaces’ convergent issues, and their derivatives with respect to time can be obtained as(21)V˙1=S1S˙1+1ε1D~1D~˙1+KΩ1Ω1Ω˙1=S1-Ucx2+D1-z˙d-1ε1D~1D^˙1+KΩ1Ω1S1=S1-UcS2-Ucα1+D1-z˙d-1ε1D~1D^˙1+KΩ1Ω1S1=S1-UcS2-k1S1+D~1-KΩ1Ω1-1ε1D~1D^˙1+KΩ1Ω1S1=-k1S12-UcS1S2.IfS2=0, we can conclude that V1=-k1S12≤0, which means S1 gradually converges to zero. Then the next step is to study(22)V˙2=V˙1+S2S˙2=-k1S12-UcS1S2+S2S3+α2-α˙1=-k1S12-UcS1S2+S2S3-k2S2+α˙1+UcS1-α˙1=-k1S12-k2S22+S2S3.In the same way, ifS3=0, then we can conclude that V2=-k1S12-k2S22≤0, which means S2 gradually converges to zero as well. From (17), it is easy to know that S3=0 is equivalent to σ=0; the key point is to verify the convergence of terminal sliding mode surface σ.From (8), (13), (15), (17), and (19), we can obtain (23)σ˙=S˙3+m1sigr1S3+m2sigr2S3=x˙3-α˙2+m1sigr1S3+m2sigr2S3=M1x3+M2sin⁡x2+M3δs+M4x3x3+M5cos⁡x2+D^2+Ψ-α˙2+m1sigr1S3+m2sigr2S3=-LweightWσsign⁡σ+Ψ~.From (7), (18), and (23), we can obtain(24)V˙3=σσ˙+D~2D~˙2+1ε2Ψ~Ψ~˙=σ-LweightWσsign⁡σ+Ψ~-h3D~22-1ε2Ψ~Ψ^˙=-LweightWσσ-h3D~22≤0.Considering the fact thatV3≥0,V˙3≤0, it is easy to know that terminal sliding mode surface σ is reachable. Once σ=0, we can know that S3 will converge rapidly to zero in a finite period. Due to (22), S2 converges to zero as well when S3=0; and once S2=0, it implies that S1 converges to zero quickly. All above analysis proves that all closed-loop sliding mode surfaces will converge to the equilibrium points in finite time.


## 4. Simulation Experiment

In this section, simulations are presented to verify the effectiveness of designed controller, which is ATSMC with NDO. The method is applied into the depth control of REMUS AUV. REMUS is a small-size, low-cost moving platform applied into various oceanographic activities; it is developed by both Massachusetts Institute of Technology (MIT) and Woods Hole Oceanographic Institution. The parameters about REMUS can be obtained from [7]; here some parameters which will be used in this paper are shown in Tables 1 and 2.Table 1
Hydrodynamic parameters of REMUS AUV.
Variables
Values
Units
M
q
˙
−4.88
kg m2/rad
M
w
w
3.18
kg
M
u
u
−6.15
kg/rad
M
u
w
24
kg
Z
q
˙
−1.93
kg m/rad
Z
q
q
−0.632
Kg m/rad2
Z
u
q
−5.22
kg/rad
M
w
˙
−1.93
kg m
M
q
q
−188
kg m2/rad2
M
u
q
−2
kg m/rad
Z
w
˙
−35.5
kg
Z
w
w
−131
kg/m
Z
u
w
−28.6
kg/m
Z
u
u
−6.15
kg/(m rad)Table 2
Physical parameters of REMUS AUV.
Variables
Values
Units
x
W
0
m
z
W
0.0196
m
y
B
0
m
W
e
i
g
299
N
m
30.48
k
g
y
W
0
m
x
B
0
m
z
B
0
m
B
u
o
306
N
I
y
y
3.45
Kg m2Case 1.
In order to undergo the simulation, due to Section3, the parameters of NDO are chosen as h1=h2=h3=1; the parameters of ATSMC are set as k1=1.2,k2=1.8,k3=1.1,ε1=0.1,ε2=0.01,m1=m2=1.1, r1=1.2, r2=0.5, LW=-1, Kσ=1, Lweight=1, andKΩ1=0.5. The initial values of state variables are [w(0),θ(0),z(0),p(0)]=[0,0,4,0] for the REMUS AUV. The external disturbance exposed on REMUS is assumed to be in the following form Dout=20sin⁡(πt/10)+10sin⁡(πt/15)+6cos⁡(πt/20+π/2) during the time period 20s≤t<40s. The surge velocity u holds constant with Uc=1.54m/s. The desired depth of AUV is defined as a time varying function zd=5+1.5sin⁡(πt/10). The task of the designed ATSMC based on NDO is to achieve the depth trajectory tracking of REMUS AUV in reference to desired depth zd.
Figure3 shows the depth tracking process of underactuated AUV, and the changing trend of rudder control input δs. We can obtain that the tracking error of depth can be nearly zero in the stable procedure, even when the AUV suffers from unknown external disturbance Dout during 20s≤t<40s. All these verify that the AUV could meet depth tracking requirements. In addition, the control input obtained from the proposed method is smooth, and its chattering phenomenon is seldom vanished.
Figure4 implies the changing trends of variables w,θ,p during the depth tracking process. It is easy to notice that the pitch angle θ and pitch angle velocity p are both forced to be in the scope of a small value; besides, the heave velocity w keeps almost as close to zero in the overall process, which satisfies the assumption that w≈0,w˙≈0.
The main disturbance for AUV is external disturbanceDout. To reduce external disturbance’s effect on AUV, the proposed controller introduces the NDO to observe and predict Dout. Another important relationship is Dout=(Iyy-Mq˙)D2; it is convenient to estimate D2 based on NDO in the controller design. We can have the observation value D^out due to NDO from Figure 5. Another part of disturbance is the constructed internal disturbance D1 which is established for convenience of designing proposed controller. An adaptive law for D1 is also introduced to accelerate the convergent speed of controller; its estimation process is shown in Figure 6.
The traits of sliding mode surfaces in the controller building procedure are vital to the convergence of the whole control system. The internal relationships between each sliding mode surface can be known from Section3.3. The change laws of sliding mode surfaces are shown in Figure 7; we can know that sliding mode surfaces S1,S2,S3 converge to equilibrium points. Another important aspect is the terminal sliding mode surface σ, which can be reachable from Figure 8. σ guarantees the convergence of Si(i=1,2,3). From (19) and Figure 9, we can understand that weighted value Wσ is used to weaken the chattering issue which occurs frequently in the traditional sliding mode control, and its value changes with σ, and apparently no strong oscillation happens to the rudder in the tracking procedure from Figure 3.Figure 3
Depth trajectory and rudder inputδs of REMUS AUV.Figure 4
State variables changing trend of REMUS AUV.Figure 5
External disturbanceDout and its observation value D^out.Figure 6
Internal disturbanceD1 and its estimation D^1.Figure 7
Sliding mode surfacesS1,S2,S3.Figure 8
Terminal sliding mode surfaceσ.Figure 9
Weighted value ofWσ.Case 2.
In order to compare with the proposed method, a traditional sliding mode controller and a proposed controller which lacks integer partΩ1 in (11) are separately applied into accomplishing the depth tracking task. Their control performances are shown, respectively, in Figures 10 and 11. Among them, the traditional sliding mode control law [26] can be expressed with the expression of (19) when m1=m2=0,Wσ=1. Through comparing the performance shown in Figure 3 with Figure 10, it is obvious that m1sigr1(S3),m2sigr2(S3) and the change rule of Wσ play an important role in reducing chattering issue of rudder input; when KΩ1=0, the rudder input of the ATSMC based on NDO can make AUV track the desired depth, but a small tracking error always exists by contrasting the control effect of Figure 3 with Figure 11; this illustrates that integer part Ω1 is necessary to achieve the expected tracking effect.Figure 10
Traditional sliding mode control method.Figure 11
ATSMC based on NDO without integer partΩ1.


## 5. Conclusion

A methodology combined with ATSMC and NDO was presented in this paper. For the convenience of design, both the kinematics and dynamics equations of underactuated AUV in the vertical plane were simplified firstly based on some assumptions. Then, a nonlinear disturbance observer (NDO) was built, and another form of AUV’s state variables equation was reconstructed on the basis of NDO. Furthermore, an ATSMC with NDO was designed, and the controller’s stability was verified according to Lyapunov’s theory and backstepping technique. Through the simulation experiments of AUV, the designed controller was proved to make the underactuated AUV track the expected time-varying depth trajectory, and it showed strong robustness to external disturbance.



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*Source: 6590517-2016-01-05.xml*