Data‐driven optimal fault‐tolerant‐control and detection for a class of unknown nonlinear discrete‐time systems

Abstract

The design of optimal fault-tolerant control with actuator and sensor faults is investigated. The system dynamics and faults are considered as a class of unknown nonlinear discrete-time systems when the data-driven equivalent model is formulated by a multi-input fuzzy rule emulated network (MiFREN).The multi-gradient learning law is developed with the proposed fault-detection algorithm to enchant the performance of MiFREN. By employing only pieces of information from the equivalent model, the proposed controller is designed and the closed-loop performance is analyzed through the rigorous theorem. Simulation systems and comparison results are provided to validate the performance of the proposed scheme.