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Abstract

In this paper, an iterative adaptive dynamic programming algorithm is proposed to deal with the optimal trajectory-tracking control problems for autonomous underwater vehicle. Two iteration procedures are used in the method, which are the i-iteration and the j-iteration.The i-iteration aims to obtain iterative trajectory-tracking control laws and the j-iteration aims to obtain iterative value functions in the i-iteration. The optimal tracking control problem is converted into an optimal regulation problem by system transformation. Then the optimal regulation problem is solved by the policy iteration adaptive dynamic programming algorithm. The policy iteration algorithm is the interacting policy and value iteration algorithms. The neural networks are used to realize the proposed algorithm and the convergence and optimality properties of the proposed algorithm are analysed. Finally, simulation example is given to show the performance of the iterative adaptive dynamic programming algorithm.

Keywords

adaptive dynamic programming autonomous underwater vehicle discrete-time neural network trajectory-tracking control

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References

Yuh , Design and control of autonomous underwater robots: A survey, Auton Robots 8 (2000), 7–24.

Xiang ,

Lapierre and

Jouvencel , Smooth transition of AUV motion control: From fully-actuated to under actuated configuration, Robot Auton Syst 67 (2015), 14–22.

Santhakumar and

Asokan , Power efficient dynamic station keeping control of a flat-fish type autonomous underwater vehicle through design modifications of thruster configuration, Ocean Eng 58 (2013), 11–21.

Wang ,

Gu ,

Gao , et al., Multivariable output feedback adaptive terminal sliding mode control for underwater vehicles, Asian J Control 18 (2016), 247–265.

Xu ,

Wang and

Qiao , Dynamical sliding mode control for the trajectory tracking of underactuated unmanned underwater vehicles, Ocean Eng 105 (2015), 54–63.

Yan ,

Yu ,

Zhang , et al., Globally finite-time stable tracking control of underactuated UUVs, Ocean Eng 107 (2015), 132–146.

Peng ,

Wang ,

Wang , et al., Distributed coordinated tracking of multiple autonomous underwater vehicles, Nonlinear Dyn 78 (2014), 1261–1276.

Wang ,

Zhang ,

P.A.

Wilson , et al., Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault, Ocean Eng 110 (2015), 15–24.

Rout and

Subudhi , A backstepping approach for the formation control of multiple autonomous underwater vehicles using a leader-follower strategy, J Mar Eng Technol 15 (2016), 38–46.

10.

Yu and

Man , Model reference adaptive control systems with terminal sliding modes, Int J Control 64 (1996), 1165–1176.

11.

Li ,

Wang and

Zhang , Finite-time output feedback tracking control for autonomous underwater vehicles, IEEE J Oceanic Eng 40 (2015), 727–751.

12.

Liu ,

Wang ,

Zhao , et al., Neural-network-based optimal control for a class of unknown discrete-time nonlinear systems using globalized dual heuristic programming, IEEE Trans Autom Sci Eng 9 (2012), 628–634.

13.

Wei ,

Liu ,

Shi , et al., Optimal multi-battery coordination control for home energy management systems via distributed iterative adaptive dynamic programming, IEEE Trans Ind Electron 42 (2015), 4203–4214.

14.

Zhang ,

Wei and

Liu , An iterative adaptive dynamic programming method for solving a class of nonlinear zero-sum differential games, Automatica 47 (2011), 207–214.

15.

Bhasin ,

Kamalapurkar ,

Johnson , et al., A novel actorcritic-identifier architecture for approximate optimal control of uncertain nonlinear systems, Automatica 49 (2013), 82–92.

16.

Zhang ,

Qing ,

Jiang , et al., Online adaptive policy learning algorithm for H1state feedback control of unknown affine nonlinear discrete-time systems, IEEE Trans Cybern 44 (2014), 2706–2718.

17.

Shi and

Mao , Multi-step control set-based nonlinear model predictive control with persistent disturbances, Asian J of Control 21 (2017), 1–11.

18.

Lin ,

Wei and

Liu , A novel optimal tracking control scheme for a class of discrete-time nonlinear systems using generalised policy iterative adaptive dynamic programming algorithm, Int J Syst Sci 48 (2017), 525–534.

19.

Mansour ,

Wu and

Hwang , Nonlinear trajectorytracking control of an autonomous underwater vehicle, Ocean Eng 145 (2017), 188–198.

Nonlinear trajectory-tracking control for autonomous underwater vehicle based on iterative adaptive dynamic programming

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