Adaptive recursive sliding mode control for surface vessel trajectory tracking with input and output constraints

To solve the trajectory tracking problem of fully-actuated surface vessel with input and output constraints, a method of adaptive neural network recursive sliding mode dynamic surface control, based on time-varying asymmetric barrier Lyapunov function, is proposed in the presence of uncertain ship model parameters and unknown external environmental disturbances. In this strategy, the asymmetric barrier Lyapunov function is designed to restrict the actual ship trajectory within limited areas. An minimal learning parameter (MLP) neural network based is introduced to approximate the model uncertainty and to reduce the computational complexity of the system. Then the command filter is applied to restrain the amplitude of input signal and avoid the problem of complicated calculations caused by backstepping. Based on that, the recursive sliding mode method is incorporated into dynamic surface control to enhance the system robustness. The hyperbolic tangent function and the Nussbaum function are introduced to compensate for nonlinear terms caused by saturation function and ensure the system stability. Finally, the application of Lyapunov function proves that all signals in the closed-loop tracking system can be guaranteed the uniformly ultimate boundedness by the proposed control law. The simulation results show that the proposed controller can effectively solve the problem of control input constraints and output constraints, and enhance the system robustness against model uncertainty and unknown external environmental disturbances. © 2020, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.