Composite Trajectory Tracking Control of Unmanned Surface Vehicles with Disturbances and Uncertainties

To attenuate the effect of uncertainties and unknown disturbances, a composite trajectory tracking control scheme using disturbance observer and neural networks (NN) is proposed for an unmanned surface vehicle (USV) in this paper. In the absence of uncertainties and unknown disturbances, by defining a nonsingular terminal sliding mode (NTSM) manifold, a NTSM-based controller is designed for the USV to guarantee the tracking errors exactly converge to zero within a finite time. In the presence of uncertainties and unknown disturbances, NN is employed to compensate uncertainties while a disturbance observer is applied to simultaneously observe NN approximation error and unknown disturbances. Simulation studies demonstrate the effectiveness of the proposed control scheme.

[1]  Zhongjiu Zheng,et al.  Global Asymptotic Model-Free Trajectory-Independent Tracking Control of an Uncertain Marine Vehicle: An Adaptive Universe-Based Fuzzy Control Approach , 2018, IEEE Transactions on Fuzzy Systems.

[2]  Zhihong Man,et al.  Non-singular terminal sliding mode control of rigid manipulators , 2002, Autom..

[3]  Chintae Choi,et al.  Practical Nonsingular Terminal Sliding-Mode Control of Robot Manipulators for High-Accuracy Tracking Control , 2009, IEEE Transactions on Industrial Electronics.

[4]  Meng Joo Er,et al.  Direct Adaptive Fuzzy Tracking Control of Marine Vehicles With Fully Unknown Parametric Dynamics and Uncertainties , 2016, IEEE Transactions on Control Systems Technology.

[5]  Shixi Hou,et al.  Robust adaptive nonsingular terminal sliding mode control of MEMS gyroscope using fuzzy-neural-network compensator , 2017, Int. J. Mach. Learn. Cybern..

[6]  Wei Zhang,et al.  Finite-time chaos control via nonsingular terminal sliding mode control , 2009 .

[7]  Yong Wang,et al.  Nonsingular Terminal Sliding Mode Based Trajectory Tracking Control of an Autonomous Surface Vehicle with Finite-Time Convergence , 2017, ISNN.

[8]  Yuri B. Shtessel,et al.  Smooth second-order sliding modes: Missile guidance application , 2007, Autom..

[9]  Meng Joo Er,et al.  Adaptive Robust Online Constructive Fuzzy Control of a Complex Surface Vehicle System , 2016, IEEE Transactions on Cybernetics.

[10]  Warren E. Dixon,et al.  Tracking and regulation control of an underactuated surface vessel with nonintegrable dynamics , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[11]  Jangmyung Lee,et al.  Finite-time sliding surface constrained control for a robot manipulator with an unknown deadzone and disturbance. , 2016, ISA transactions.

[12]  Peter J. Gawthrop,et al.  A nonlinear disturbance observer for robotic manipulators , 2000, IEEE Trans. Ind. Electron..

[13]  Fuchun Sun,et al.  Disturbance Observer Based Composite Learning Fuzzy Control of Nonlinear Systems with Unknown Dead Zone , 2017, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[14]  Thor I. Fossen,et al.  Marine Control Systems Guidance, Navigation, and Control of Ships, Rigs and Underwater Vehicles , 2002 .

[15]  Syuan-Yi Chen,et al.  Robust Nonsingular Terminal Sliding-Mode Control for Nonlinear Magnetic Bearing System , 2011, IEEE Transactions on Control Systems Technology.

[16]  S. Bhat,et al.  Finite-time stability of homogeneous systems , 1997, Proceedings of the 1997 American Control Conference (Cat. No.97CH36041).

[17]  Zhong-Ping Jiang,et al.  Global tracking control of underactuated ships by Lyapunov's direct method , 2002, Autom..

[18]  Simon X. Yang,et al.  An efficient neural network approach to tracking control of an autonomous surface vehicle with unknown dynamics , 2013, Expert Syst. Appl..

[19]  Meng Joo Er,et al.  A Novel Extreme Learning Control Framework of Unmanned Surface Vehicles , 2016, IEEE Transactions on Cybernetics.

[20]  Ye Yan,et al.  Neural network approximation-based nonsingular terminal sliding mode control for trajectory tracking of robotic airships , 2016 .