Adaptive Fault‐Tolerant Prescribed Performance Control for Underactuated Unmanned Surface Vehicles

Abstract

This paper presents a novel adaptive fault‐tolerant prescribed performance control scheme for trajectory tracking of underactuated unmanned surface vehicles (USVs) operating under actuator faults and external disturbances. The proposed control framework integrates prescribed performance control (PPC) with radial basis function neural networks (RBF‐NNs) and barrier Lyapunov functions (BLFs) to guarantee tracking errors remain within predefined performance bounds while compensating for both multiplicative and additive actuator faults. A key innovation lies in the incorporation of a smooth shifting function that enables the controller to handle initial constraint violations, thereby significantly expanding the operational envelope compared to conventional PPC approaches. The adaptive laws are designed to simultaneously estimate unknown system dynamics, external disturbances, and fault parameters without requiring explicit fault detection and isolation mechanisms. Comprehensive simulation studies on a full‐scale USV model demonstrate that the proposed controller achieves superior tracking performance with position errors converging to steady‐state values below 0.04 m and yaw errors below 0.03 rad, even under severe actuator degradation scenarios where effectiveness factors drop to 50% and significant bias faults are present. The controller maintains prescribed performance bounds throughout nine distinct fault events occurring over a 120‐s mission, validating its robustness and fault‐tolerant capabilities.