Adaptive fuzzy event-triggered fault-tolerant control for time-varying delay switched nonlinear systems with actuator and sensor failures

IF 3.2 3区计算机科学 Q2 AUTOMATION & CONTROL SYSTEMS

International Journal of Robust and Nonlinear Control Pub Date : 2024-07-25 DOI:10.1002/rnc.7560

Fang Wang, Shi Li

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引用次数: 0

Abstract

This article addresses the problem of adaptive fault-tolerant event-triggered control (ETC) for time-varying delay switched nonlinear systems (SNSs) with actuator and sensor failures. First, in order to address the unknown actuator failures, a fault compensation coefficient is introduced. Then, a mode-dependent state observer is established to estimate the unknown states. The unknown nonlinear function is approximated by the usage of fuzzy logic systems (FLS). In addition, the Lyapunov–Krasovskii functions are utilized to discuss the influence of time-varying delays. Then, an adaptive fuzzy fault-tolerant ETC and a novel event-triggering mechanism (ETM) are derived. It is proved that the proposed fault-tolerant controller can ensure that all states of the closed-loop system (CLS) are semi-globally uniformly ultimately bounded (SGUUB) by average dwell time (ADT) constraints and the Zeno phenomenon can be excluded. Results of two simulation examples verify that the proposed control method is effective.

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针对具有执行器和传感器故障的时变延迟开关非线性系统的自适应模糊事件触发容错控制

本文探讨了具有执行器和传感器故障的时变延迟开关非线性系统（SNS）的自适应容错事件触发控制（ETC）问题。首先，为了解决未知致动器故障问题，本文引入了故障补偿系数。然后，建立一个与模式相关的状态观测器来估计未知状态。利用模糊逻辑系统（FLS）对未知非线性函数进行近似。此外，还利用 Lyapunov-Krasovskii 函数来讨论时变延迟的影响。然后，推导出一种自适应模糊容错 ETC 和一种新型事件触发机制 (ETM)。实验证明，所提出的容错控制器能确保闭环系统（CLS）的所有状态在平均停留时间（ADT）约束下都是半全局均匀终极约束（SGUUB），并能排除芝诺现象。两个仿真实例的结果验证了所提出的控制方法是有效的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Robust and Nonlinear Control 工程技术-工程：电子与电气

CiteScore

6.70

自引率

20.50%

发文量

505

审稿时长

2.7 months

期刊介绍： Papers that do not include an element of robust or nonlinear control and estimation theory will not be considered by the journal, and all papers will be expected to include significant novel content. The focus of the journal is on model based control design approaches rather than heuristic or rule based methods. Papers on neural networks will have to be of exceptional novelty to be considered for the journal.