Adaptive Neural Network Iterative Sliding Mode Course Tracking Control for Unmanned Surface Vessels

Abstract

In view of the problem of course tracking control of under-driven USV under complex external environment, the adaptive control law is designed by constructing an iterative sliding mode function and using Lyapunov stability theory on the basis of the kinetic model of ship motion. The RBF neural network control technology and adaptive control technology are integrated into the control algorithm, and the iterative sliding mode heading tracking controller of the unmanned surface ship adaptive-neural network is designed. The online reinforcement learning of the RBF neural network is carried out by the reinforcement learning algorithm, which enhances the approximation performance of the network. Besides, the particle swarm optimization with shrinkage factor is applied to the optimization for control parameters to enhance the adaptability and robustness of the control system. The performance experiment verifies the effectiveness and practicability of the adaptive iterative sliding mode control algorithm for unmanned surface ships based on reinforcement learning and particle swarm optimization algorithm; meanwhile, comparative tracking experiments verify that the comprehensive performance of the proposed USV’s course tracking control system is better than that of the genetically optimized neural network sliding mode control system. Therefore, the fusion of multiple algorithms can be applied to improve the performance of the control system.

Robust Fixed-Time Fault-Tolerant Control for USV with Prescribed Tracking Performance

“…At present, the trajectory tracking control methods of the USV mainly include the following: PID control [3], backstepping control [4,5], fuzzy control [6], adaptive control [7,8], and sliding mode control (SMC) [9][10][11]. In particular, SMC has been proven to be highly robust to uncertainties and disturbances in nonlinear systems, and it is widely used in ship trajectory tracking control.…”