Performance-constrained fault-tolerant DSC based on reinforcement learning for nonlinear systems with uncertain parameters

Abstract

In this paper, a performance-constrained fault-tolerant dynamic surface control (DSC) algorithm based on reinforcement learning (RL) is proposed for nonlinear systems with unknown parameters and actuator failures. Considering the problem of multiple actuator failures, the bound for sum of the failure parameters are estimated rather than the parameters themselves, an infinite number of actuator failures can be handled. To improve the performance of the system, based on actor-critic neural networks (NNs) and optimized backstepping control (OBC), RL is introduced to optimize the tracking errors and inputs. By introducing an intermediate controller, the controllers derived from RL algorithm and the fault-tolerant controller are isolated, the difficulties of using RL in fault-tolerant control (FTC) are reduced. In addition, an initial unbounded boundary function is used so that the initial value of the error does not need to be within a prescribed range, not only the tracking error can be reduced to the prescribed accuracy, but also all closed-loop signals are bounded. Finally, the effectiveness and advantages of the proposed algorithm are verified by two examples.