Fault tolerant control for a class of nonlinear systems with multiple faults using neuro-dynamic programming

Considering the multiple fault scenario, i.e., both actuator and sensor faults occur simultaneously, this paper develops a neuro-dynamic programming (NDP)-based fault tolerant control (FTC) scheme for a class of nonlinear systems. By combining a descriptor observer with an adaptive observer, system states and multiple faults are estimated simultaneously. For the nominal system, i.e., the fault-free system, a critic neural network (NN) is employed to solve the Hamilton-Jacobi-Bellman (HJB) equation, and the approximate optimal control policy is obtained. To eliminate the influence of sensor faults, the accurate estimations of system states are used instead of system states from faulty sensors to construct the approximate optimal control policy. Then, by combining the estimations of actuator faults with the approximate optimal control policy, an FTC law is developed to suppress the influence of actuator faults. The stability of the closed-loop nonlinear system is analyzed to be uniformly ultimately bounded via the Lyapunov stability theorem. The effectiveness of the present FTC scheme is demonstrated by two simulation examples.

Automated summary

This paper proposes a neuro-dynamic programming-based fault tolerant control scheme for a class of nonlinear systems, considering the occurrence of both actuator and sensor faults simultaneously. The scheme combines a descriptor observer with an adaptive observer to estimate system states and multiple faults. By employing a critic neural network, the approximate optimal control policy is obtained for the fault-free system. An FTC law is developed to suppress the influence of actuator faults by combining the estimations of actuator faults with the approximate optimal control policy. The stability of the closed-loop nonlinear system is analyzed using the Lyapunov stability theorem.