Observer‐based single‐network incremental adaptive dynamic programming for fault‐tolerant control of nonlinear systems with actuator faults

Abstract

Summary A model‐free incremental adaptive fault‐tolerant control (FTC) scheme is proposed for a class of nonlinear systems with actuator faults. To deal with actuator faults and guarantee the approximate optimal performance of the nominal nonlinear system without any prior knowledge of system dynamics, a single‐network incremental adaptive dynamic programming (SIADP) algorithm based on incremental neural network observer is developed to design an active fault‐tolerant control (AFTC) policy. An approximate linear time‐varying system is obtained by incremental nonlinear technique, in which the relevant matrix parameters are identified by recursive least square estimation. Then, a SIADP algorithm‐based fault‐tolerant controller is developed. Based on the redundancy characteristic and function of actuators, a grouping scheme of actuators is introduced. An incremental neural network observer is designed to approximate the actuator faults. The novel SIADP scheme is constructed with a simplified single critic neural network to shorten the learning time and decrease the computational burden in the control process, in which the norm of the weight estimations of critic neural network is updated. Moreover, based on the Lyapunov theorem, the uniformly ultimately bounded stability of the closed‐loop incremental system is proved. Finally, simulations are given to verify the effectiveness of the proposed FTC scheme.