Event-triggered adaptive reliable guaranteed performance control for uncertain nonlinear systems with abrupt non-affine faults

Zeyang Li, Jiuxiang Dong and Changjiang Xi

Applied Mathematics and Computation, 2020, vol. 380, issue C

Abstract: In this paper, the issue of neural-network-approximation-based adaptive reliable event-triggered tracking for a class of strict-feedback systems with abrupt non-affine fault is investigated. The mean-value theorem is first used to decouple non-affine fault into affine form and then the Nussbaum function is introduced to overcome the difficulty of unknown control gains encountered in controller design process. Under the framework of adaptive backstepping technique, a novel event-triggered fault-tolerant control scheme is proposed, which ensures the stability of the system and reduces the waste of transmission resources. It should be mentioned that abrupt actuator fault and the discontinuous control signals under the event-triggered mechanism may lead to poor transient performance. Therefore, the prescribed performance bounds (PPB) method is used to constrain the tracking error within the desired specified range, which successfully improves the transient performance of the system. Finally, it is proved that all closed-loop signals under the proposed control method are semi-globally bounded and the Zeno behavior is avoided. The simulation results illustrate the effectiveness of the proposed method.

Keywords: Adaptive backstepping control; Event-triggered control; Abrupt non-affine faults; Radial basis function neural networks; Nonlinear strict-feedback systems (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:380:y:2020:i:c:s0096300320302253

DOI: 10.1016/j.amc.2020.125256

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