Fuzzy Optimal Tracking Control for Autonomous Surface Vehicles With Prescribed-Time Convergence Analysis

In this article, we investigate the prescribed-time fuzzy optimal tracking control for autonomous surface vehicles (ASVs). A monotonically decreasing boundary function that incorporates the settling time and tracking accuracy is proposed. A coordinate transformation on the boundary function and tracking error is proposed, and then an augmented system is defined. Subsequently, a new performance index function is presented that considers both the prescribed performance costs and control input costs. Given the inherent difficulties when directly resolving the Hamilton–Jacobi–Bellman equation within the prescribed-time framework, a new fuzzy optimal control scheme is proposed via integral reinforcement learning. This scheme does not require knowledge on the drift dynamics in the designed control policy and tuning laws, guarantees the simultaneous approximation of the optimal value function and control policy, ensures the stability of the ASV system, and allows users to specify the settling time and tracking accuracy. Finally, the presented strategy's effectiveness is validated by simulation.