Event-triggered scheme for fault detection and isolation of non-linear system with time-varying delay

• Published in: IET control theory & applications Vol. 14; no. 16; pp. 2429 - 2438
• Main Authors: Aslam, Muhammad Shamrooz, Ullah, Rizwan, Dai, Xisheng, Sheng, Andong
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 05.11.2020
• Subjects: asymptotic stability; control system synthesis; delayed nonlinear systems; delays; ET delay NCSs; ET mechanism; event condition; event‐triggered mechanism; event‐triggered scheme; fault detection; fault diagnosis; linear matrix inequalities; linear systems; Lyapunov methods; mode observers; networked control systems; nonlinear control systems; nonlinear plant; nonlinear system; observers; Research Article; sampled data; sensor fault; SMOs; stability criteria; subsystem B; time‐varying delay; time‐varying systems; uncertain systems; time-varying systems; fault diagnosis; sampled data; fault detection; event-triggered mechanism; nonlinear system; asymptotic stability; mode observers; linear matrix inequalities; subsystem B; event condition; sensor fault; time-varying delay; nonlinear plant; uncertain systems; stability criteria; control system synthesis; linear systems; event-triggered scheme; delayed nonlinear systems; nonlinear control systems; delays; ET mechanism; SMOs; networked control systems; ET delay NCSs; observers; Lyapunov methods
• DOI: 10.1049/iet-cta.2018.5469
• ISSN: 1751-8644

In this article, we investigate a new way of dealing with fault detection and isolation (FDI) for a class of delayed non-linear systems with an event-triggered mechanism (ETM). This article is based on 1) To estimate the dynamics of the nonlinear plant, Sliding Mode Observers (SMO's) is manipulated with Luenberger. 2) Under the ETM, sampled data is released only when the plant's measurement (sampled) violate the specific threshold of the event condition. 3) First, transform into two subsystems. In subsystem-A contains uncertainties without sensor fault and vice-versa in subsystem-B. To handle the uncertainties that appeared in subsystem-A, a new SMO's implemented while traditional Luenberger is employed to detect the sensor fault. Then, by using a new Lyapunov-Krasovskii function approach performance index is proposed. Based on stability criteria, sufficient conditions are provided in the account of Linear Matrix Inequalities (LMIs). The simulation example is given to demonstrate the effectiveness of our design method.