首页> 外文期刊>Journal of the Franklin Institute >A new estimation/decoupling approach for robust observer-based fault reconstruction in nonlinear systems affected by simultaneous time varying actuator and sensor faults
【24h】

A new estimation/decoupling approach for robust observer-based fault reconstruction in nonlinear systems affected by simultaneous time varying actuator and sensor faults

机译:同时变化致动器和传感器故障影响的非线性系统中鲁棒观察者故障重建的新估计/解耦方法

获取原文
获取原文并翻译 | 示例
       

摘要

This paper presents an estimation/decoupling approach to design a fault estimation observer for nonlinear systems affected by a simultaneous actuator and sensor faults. In this approach, the sensor fault is treated as unknown inputs while the state and the actuator faults are estimated by recalling the extended state observer (ESO) idea. A Takagi-Sugeno Multiple-Integral Unknown Input Observer (TSMIUIO) is used to achieve this approach. First, the TSMIUIO estimates an extended state consists of the original system state and the actuator faults by using the ESO idea. On the other hand, the sensor faults are decoupled from the estimation of the extended state by treating such faults as unknown inputs. Then, the TSMIUIO permits an implicit estimation of sensor faults by using the output signals. The benefits earned by such an approach are: (1) the approach does not presume constraints on the time behaviour of the sensor faults. (2) By decoupling the sensor faults, the approach eliminates the bi-directional interaction (BDI) between the estimated signals. (3) It offers an implicit estimation of sensor faults by using the output signals. The design algorithm is given in a linear matrix inequality (LMI) form. The single-link flexible joint robot and the FAST 5MW benchmark wind turbine have been used to show the effectiveness of the proposed method. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.
机译:本文介绍了设计由同时执行器和传感器故障影响的非线性系统故障估计观察者的估计/去耦方法。在这种方法中,通过调用扩展状态观察者(ESO)想法,估计传感器故障作为未知输入。使用Takagi-Sugeno多积分未知输入观察者(TSMIIO)来实现这种方法。首先,TSMIUIO估计扩展状态由使用ESO IDEA构成原始系统状态和执行器故障。另一方面,传感器故障通过将这些故障视为未知输入来从扩展状态的估计解耦。然后,TSMIIO允许通过使用输出信号来隐化传感器故障的估计。这种方法所获得的益处是:(1)该方法在传感器故障的时间行为上没有提出限制。 (2)通过去耦传感器故障,该方法消除了估计信号之间的双向交互(BDI)。 (3)它通过使用输出信号提供传感器故障的隐含估计。设计算法以线性矩阵不等式(LMI)形式给出。单链路柔性联合机器人和快速的5MW基准风力涡轮机已被用来显示所提出的方法的有效性。 (c)2020富兰克林学院。 elsevier有限公司出版。保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号