Fast actuator and sensor fault estimation based on adaptive unknown input observer

Sheng Gao; Guangfu Ma; Yanning Guo; Wei Zhang

doi:10.1016/j.isatra.2022.01.019

Fast actuator and sensor fault estimation based on adaptive unknown input observer

ISA Trans. 2022 Oct;129(Pt A):305-323. doi: 10.1016/j.isatra.2022.01.019. Epub 2022 Jan 27.

Authors

Sheng Gao¹, Guangfu Ma², Yanning Guo³, Wei Zhang⁴

Affiliations

¹ Department of Control Science and Engineering, Harbin Institute of Technology, China; State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China. Electronic address: 19B904072@stu.hit.edu.cn.
² Department of Control Science and Engineering, Harbin Institute of Technology, China. Electronic address: magf@hit.edu.cn.
³ Department of Control Science and Engineering, Harbin Institute of Technology, China. Electronic address: guoyn@hit.edu.cn.
⁴ State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, China; Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, China. Electronic address: zhangwei@sia.cn.

PMID: 35151486
DOI: 10.1016/j.isatra.2022.01.019

Abstract

This study evaluates the robust fault estimation problem of systems with actuator and sensor faults though the simultaneous use of unknown input disturbances and measurement noise. Specifically, an augmented descriptor system is preliminarily developed by creating an augmented state consisting of system states and sensor faults. Next, a novel fast adaptive unknown input observer (FAUIO) is proposed for the system to enhance its fault estimation performance. The existence condition of the novel FAUIO is then introduced for linear time-invariant systems with unknown input disturbances. Furthermore, the proposed FAUIO is extended to a class of Lipschitz nonlinear systems with unknown input disturbances and measurement noise to investigate the robust fault estimation problem. Accordingly, an H_∞ performance index is employed to attenuate the influence of disturbances on fault estimation. Moreover, the linear matrix inequality (LMI) technique is applied to solve the designed FAUIO. Finally, the effectiveness of the developed FAUIO is validated via the simulation of two examples.

Keywords: Fast adaptive unknown input observer (FAUIO); Fault diagnosis; Fault estimation; Linear matrix inequality (LMI); Nonlinear system.