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中国工业与应用数学学会会刊
主管:中华人民共和国教育部
主办:西安交通大学
ISSN 1005-3085  CN 61-1269/O1

工程数学学报

• • 上一篇    下一篇

一类带有未知控制方向的非线性系统的模糊自适应事件触发容错控制

阎  岩1,   武力兵1,   赵楠楠2,   张瑞艳1   

  1. 1. 辽宁科技大学理学院,鞍山 114051;2. 辽宁科技大学电子与信息工程学院,鞍山 114051
  • 出版日期:2022-12-15 发布日期:2022-12-15
  • 通讯作者: 武力兵 E-mail: beyondwlb@163.com
  • 基金资助:
    国家自然科学基金 (61773221; 61773013).

Fuzzy Adaptive Event-triggered Fault-tolerant Control for a Class of Nonlinear Systems with Unknown Control Directions

YAN Yan1,   WU Libing1,   ZHAO Nannan2,   ZHANG Ruiyan1   

  1. 1. School of Science, University of Science and Technology Liaoning, Anshan 114051;
    2. School of Electronic and Information Engineering, University of Science and Technology Liaoning, Anshan 114051
  • Online:2022-12-15 Published:2022-12-15
  • Supported by:
    The National Natural Science Foundation of China (61773221; 61773013).

摘要:

针对带有未知控制方向、未知非线性函数以及执行器故障的不确定非线性系统,研究了相应的模糊自适应事件触发容错控制问题。首先,运用反步法和模糊逻辑系统 (FLS) 理论知识相结合,构造出自适应事件触发容错控制器和自适应更新律,有效地补偿了执行器故障对系统的影响。其次,在自适应事件触发容错控制器的设计中引入了 Nussbaum 函数。最后,所设计的控制方案保证了闭环信号在给定紧集内一致最终有界,且仿真结果验证了本文所提出的控制方案的有效性。

关键词: 事件触发控制, 容错控制, 自适应控制, 非线性系统, 模糊逼近

Abstract:

The problem of fuzzy adaptive event-triggered fault-tolerant control is developed for uncertain nonlinear systems with unknown control directions, unknown nonlinear functions and actuator faults. Firstly, the adaptive event-triggered fault-tolerant controller and the adaptive laws are constructed by combining the backstepping method and theoretical knowledge about fuzzy logic system, which can effectively compensate the influence of actuator faults on the system. Secondly, the Nussbaum function is introduced into the design of the adaptive event-triggered fault-tolerant controller. Finally, the control scheme is designed to ensure that the closed-loop signal is uniformly and ultimately bounded within a given compact set, and the effectiveness of the proposed control scheme is verified through simulation results.

Key words: event-triggered control, fault-tolerant control, adaptive control, nonlinear systems, fuzzy approximation

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