Adaptive neural control for marine autonomous surface ships in cross-water scenarios

Abstract

ObjectiveAn adaptive neural control (ANC) scheme with specified performance is proposed for the tracking control of marine autonomous surface ships (MASS) subject to uncertain model parameters and unknown external environmental disturbances in cross-water scenarios. MethodsUnder the back-stepping design framework, a neural network is utilized to approximate the uncertain model parameters and unknown external environmental disturbances. A novel specified performance function is constructed and combined with the barrier Lyapunov function (BLF) to transform the cross-water design, while the dynamic surface control technique is employed to reduce the system's computational complexity. Stability analysis is then performed by means of Lyapunov theory to demonstrate that all signals within the control system are bounded.ResultsThe simulation results show that the designed control scheme is not only capable of solving the cross-water tracking control of MASS, but that the tracking error can satisfy the convergence to a given bounded range within a predefined time offline. ConclusionThe results of this study can solve the cross-water tracking control problems of MASS and provide valuable references for the tracking control of ships in restricted waters, giving them practical engineering significance.

Keywords

• unmanned vehicles
• marine autonomous surface ship (mass)
• neural networks
• adaptive neural control (anc)
• barrier lyapunov function (blf)
• cross-water scenarios