Reinforcement Learning-Based Fault-Tolerant Control for Output-Constrained Nonlinear Systems With Preassigned-Time Performance

In this paper, the problem of adaptive fault-tolerant optimal control is investigated for nonstrict-feedback nonlinear systems with deferred output and performance constraints (DOPCs). By skillfully constructing the shifting transformation and finite-time constraining function, a novel error-dependent barrier function is innovated to achieve the preassigned time tracking performance without the conservative initial limitations, helping the property of DOPCs be preserved. Neural networks (NNs)-based reinforcement learning (RL) is exploited, which takes advantage of approximation means to solve the Hamiltonian equation under the backstepping framework, optimally reconciling the tracking performance and control behavior. By utilizing NNs to online estimate the unaware faults and uncertainty, an adaptive fault-tolerant optimal controller is designed. Finally, the efficiency of the proposed method is confirmed by simulations.