Global Stability of Unmanned Surface Vehicles With Optimal Thrust Allocation Using Lyapunov-Based Model Predictive Fault-Tolerant Control

Abstract

Conventional fault-tolerant control schemes, which compensate control signals based on actuator fault information, fail to reallocate control efforts to healthier ones, resulting in control inefficiency and exacerbated actuator damage. To deal with this problem, the article proposes a Lyapunov-based model predictive control (LMPC) optimization scheme that incorporates thrust allocation of unmanned surface vehicles (USVs) subject to disturbances, actuator faults, and saturations. Firstly, an auxiliary control system, incorporating the faults and disturbance observer and a sliding mode control law with an anti-windup compensator, is integrated into the LMPC framework. This integration ensures global stability and guarantees the feasibility of the optimization problem for any initial conditions, even with disturbances and actuator faults. Secondly, a fault-informed thrust allocation strategy is incorporated into the LMPC optimization framework. Upon actuator failure, the control weight is dynamically adjusted based on fault information, after which the LMPC optimization capabilities are employed to fine-tune the allocation of control signals. This approach not only optimizes thrust allocation but also reduces resource consumption while safeguarding the faulty actuator. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm.