系统工程与电子技术 ›› 2022, Vol. 44 ›› Issue (7): 2364-2373.doi: 10.12305/j.issn.1001-506X.2022.07.34

• 可靠性 • 上一篇    下一篇

基于自适应未知输入观测器的多故障快速重构

高升1,2, 马广富1, 郭延宁1,*   

  1. 1. 哈尔滨工业大学航天学院, 黑龙江 哈尔滨 150001
    2. 中国科学院沈阳自动化研究所, 辽宁 沈阳 110016
  • 收稿日期:2021-07-15 出版日期:2022-06-22 发布日期:2022-06-28
  • 通讯作者: 郭延宁
  • 作者简介:高升 (1991—), 男, 助理研究员, 博士研究生, 主要研究方向为故障诊断、机器人控制|马广富 (1963—), 男, 教授, 博士, 主要研究方向为最优控制、航天器控制|郭延宁 (1985—), 男, 教授, 博士, 主要研究方向为航天器控制、视觉导航
  • 基金资助:
    国家自然科学基金(61973100);国家自然科学基金(61876050)

Fast reconstruction of multiple faults based on adaptive unknown input observer

Sheng GAO1,2, Guangfu MA1, Yanning GUO1,*   

  1. 1. School of Astronautics, Harbin Institute of Technology, Harbin 150001, China
    2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
  • Received:2021-07-15 Online:2022-06-22 Published:2022-06-28
  • Contact: Yanning GUO

摘要:

针对一类非线性函数中耦合执行器故障的非线性动态系统, 提出一种基于自适应未知输入观测器的多故障快速重构方法, 通过引入比例项提高故障重构的快速性。首先, 将执行器故障进行解耦处理并构建包含传感器故障的增广系统。然后, 综合H性能指标给出状态估计误差的稳定性证明。接着, 将观测器增益矩阵的求解转化为受线线矩阵不等式约束的非线性优化问题, 并实现执行器故障和传感器故障的多故障重构。最后, 结合单关节柔性机器人算例仿真验证了所提方法的有效性。

关键词: 多故障诊断, 故障重构, 快速自适应未知输入观测器, 非线性系统, 线性矩阵不等式

Abstract:

A scheme of multiple faults reconstruction using the fast adaptive unknown input observer (FAUIO) is proposed, which is used for a class of nonlinear systems where the faults enter the state equations via nonlinear functions. A proportional term is used to improve the rapidity of fault reconstruction. Specifically, actuator faults should be decomposed from the nonlinear function and an augmented descriptor system is preliminarily developed by constructing an augmented state composed of system states and sensor faults. Next, an H performance index is employed to prove the robust asymptotically stability via the state estimation error produced by the observer. Then, the problem of solving the designed FAUIO is transformed into a linear matrix inequalities (LMIs) constrained nonlinear optimization problem and solved by using the linear matrix inequality optimization technique, and the multiple faults reconstruction of actuator faults and sensor faults is further realized. Finally, the effectiveness of the developed FAUIO is validated via simulations of a single-link flexible joint robot.

Key words: multiple fault diagnosis, fault reconstruction, fast adaptive unknown input observer (FAUIO), nonlinear systems, linear matrix inequality (LMI)

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