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Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based iterative learning Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 418-429页

作者： Yanzheng Zhu Nuo Xu Fen Wu Xinkai Chen Donghua Zhou IEEE College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China College of Mechanical Engineering and Automation Huaqiao UniversityXiamen 361021China Department of Mechanical and Aerospace Engineering North Carolina State UniversityRaleigh NC 27695 USA Department of Electronic and Information Systems Shibaura Institute of TechnologySaitama 337-8570Japan

In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuoustime Markov jump piecewise-affine(PWA) systems against actuator and sensor faults. Firstly, a novel mode-dependent PWA iterative learning observer with current feedback is designed to estimate the system states and faults, simultaneously, which contains both the previous iteration information and the current feedback mechanism. The auxiliary feedback channel optimizes the response speed of the observer, therefore the estimation error would converge to zero rapidly. Then, sufficient conditions for stochastic stability with guaranteed performance are demonstrated for the estimation error system, and the equivalence relations between the system information and the estimated information can be established via iterative accumulating representation.Finally, two illustrative examples containing a class of tunnel diode circuit systems are presented to fully demonstrate the effectiveness and superiority of the proposed iterative learning observer with current feedback.

关键词： Current feedback fault estimation iterative learning observer Markov jump piecewise-affine system

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Fault Diagnosis for non-Gaussian Stochastic Distribution Systems Using iterative learning observer 25th

Fault Diagnosis for non-Gaussian Stochastic Distribution Sys...

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第25届中国控制与决策会议

作者： Lina Yao Wei Cao School of Electrical Engineering Zhengzhou University

ISBN: (纸本)9781467355339

Stochastic distribution control （SDC） systems are a group of systems where the outputs considered are the measured probability density functions（PDFs）of the system output whilst subjected to a normal crisp input. The purpose of the fault diagnosis of such systems is to use the measured input and the system output PDFs to obtain possible fault information of the system. In this paper the rational square-root B-spline model is used to represent the dynamics between the output PDF and the input. The proposed approach relies on an iterative learning observer（ILO）for fault estimation. The fault may be constant, slow-varying or fast-varying.Convergency analysis is performed for the error dynamics raised from the fault diagnosis phase and simulated examples are given to show the effectiveness of the proposed algorithm.

关键词： Stochastic distribution control Fault diagnosis Rational square-root iterative learning observer

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