Continuous reinforcement learning to robust fault tolerant control for a class of unknown nonlinear systems

This paper proposes two strategies to design robust adaptive fault tolerant control (FTC) systems for a class of unknown n-order nonlinear systems in presence of actuator and sensor faults versus bounded unknown external disturbances. It is based on machine learning approaches which are continuous reinforcement learning (RL) and neural networks (NNs). In the first FTC strategy, an intelligent observer is designed for unknown nonlinear systems when faults occur or not. In the second strategy, a robust reinforcement learning FTC is proposed through combining reinforcement learning to treat the unknown nonlinear faulty system and nonlinear control theory to guarantee the stability and robustness of the system. Critic and actor of continuous RL are adopted based on the behavior of the defined Lyapunov function. In both strategies, to generate the residual a Gaussian radial basis function is used for an online estimation of the unknown dynamic function of the normal system. The adaptation law of the online estimator is derived in the sense of Lyapunov function which is defined based on adjustable parameters of the estimator and switching surfaces containing dynamic errors and residuals. Simulation results demonstrate the validity and feasibility of proposed FTC systems. (C) 2015 Elsevier B.V. All rights reserved.