Reinforcement Learning-Based Cooperative Fault-Tolerant Control for Multi-Actuator System With Uncertain Parameters and State Constraints

In this paper, a fault-tolerant control (FTC) scheme based on reinforcement learning (RL) is proposed to address the optimal FTC challenge in multi-actuator aircraft system with uncertain parameters and time-varying state constraints. The control algorithm consists of two parts, i.e., the state constraints observer based on collision detection trigger and the optimal FTC policy based on actor-critic architecture. The state constraints are caused by the actuator blockage fault. To estimate these constraints, an adaptive external torque observer and a collision detection algorithm are devised. By integrating state constraints estimation with a barrier function, a multi-actuator cooperative FTC policy based on RL is formulated. Ultimately, the efficacy of the proposed scheme is demonstrated through experimental validation tests.