Adaptive asymptotic tracking control for nonlinear systems with actuator fault and prescribed performance based on event‐triggered mechanism

Abstract

In this article, the event-triggered adaptive neural networks tracking control problem for nonlinear systems with prescribed performance and actuator fault is investigated. Meanwhile, bias faults and loss of effectiveness in actuators are taken into consideration, and the effectiveness factor is unknown. In the process of controller design under the framework of backstepping technology, neural networks are implemented to model the unknown terms of systems, and the tracking error is limited to a predefined boundary by using an error transformation. To economize communication resources, an adaptive neural networks event-triggered control (ETC) strategy is developed, which can ensure that all the closed-loop signals are bounded, and the tracking error not only confines to a prescribed function but also asymptotically converges to zero. Finally, two simulation examples are presented to further confirm the effectiveness of the proposed control strategy.