Adaptive neural output feedback fault tolerant control for a class of uncertain nonlinear systems with intermittent actuator faults

Abstract

Abstract In real applications, actuators of control systems frequently encounter unknown intermittent faults during operation while effectively handing such faults is still a challenge. In this paper, an adaptive neural output feedback fault tolerant control (FTC) scheme based on the command filtered backstepping is developed for a class of uncertain nonlinear systems to address this challenge. In this scheme, a stable nonlinear observer is designed to estimate the system states and neural networks with random hidden nodes are utilized in this observer to approximate unknown functions. A projection algorithm is adopted to estimate system unknown parameters such that the boundedness of parameter estimates is guaranteed. It is proved that the boundedness of all signals in the closed-loop system can be ensured by the proposed modified Lyapunov function. Also the ultimate bound of the tracking error depends on design parameters, adjustable jumping amplitude of Lyapunov function and minimum fault time interval. A truncated L 2 bound is established by iterative calculation to illustrate that the transient tracking error performance is determined by design parameters in the controller and observer. Applications on two simulation examples validate the effectiveness of the proposed scheme.