Event-triggered neural control for non-strict-feedback systems with actuator failures

• Published in: IET control theory & applications Vol. 13; no. 2; pp. 171 - 182
• Main Authors: Xu, Yuanyuan, Zhou, Qi, Li, Tieshan, Liang, Hongjing
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 29.01.2019
• Subjects: actuator failures; actuators; adaptive control; adaptive event‐triggered control problem; approximate unknown nonlinear functions; backstepping technique; closed loop systems; closed‐loop signals; control nonlinearities; control system synthesis; event‐triggered mechanism; event‐triggered neural control; feedback; Lyapunov methods; measurement errors; neural networks; neural observer; neural tracking controller; neurocontrollers; nonlinear control systems; nonlinear nonstrict‐feedback systems; observers; Research Article; uncertain systems; unknown state variables estimation; uncertain systems; measurement errors; adaptive event-triggered control problem; neural networks; approximate unknown nonlinear functions; neural tracking controller; control system synthesis; adaptive control; neural observer; event-triggered mechanism; closed-loop signals; event-triggered neural control; closed loop systems; feedback; backstepping technique; actuators; neurocontrollers; nonlinear control systems; control nonlinearities; actuator failures; unknown state variables estimation; nonlinear nonstrict-feedback systems; observers; Lyapunov methods
• ISSN: 1751-8644; 1751-8652
• DOI: 10.1049/iet-cta.2018.5403

This study is concerned with an adaptive event-triggered control problem for non-linear non-strict-feedback systems subject to actuator failures. For actuator failures, both total loss of effectiveness (TLOE) and partial loss of effectiveness (PLOE) are considered. The event-triggered mechanism is proposed in this study, which may influence measurement errors. Neural networks (NNs) are used to approximate unknown non-linear functions, and a neural observer is designed to estimate unknown state variables. Then a neural tracking controller is constructed to reduce the communication burden via backstepping technique. The new controller ensures that the output of the system reaches to the same trajectory with the reference signal, and it also guarantees the boundedness of all the closed-loop signals. Finally, a simulation example is used to testify the results.