Fault Estimation for a Class of Markov Jump Piecewise-Affine Systems: Current Feedback Based Iterative Learning ApproachOA北大核心CSTPCD
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.
Yanzheng Zhu;Nuo Xu;Fen Wu;Xinkai Chen;Donghua Zhou;
IEEE College of Electrical Engineering and Automation,Shandong University of Science and Technology,Qingdao 266590,ChinaCollege of Mechanical Engineering and Automation,Huaqiao University,Xiamen 361021,ChinaIEEE Department of Mechanical and Aerospace Engineering,North Carolina State University,Raleigh NC 27695 USAIEEE Department of Electronic and Information Systems,Shibaura Institute of Technology,Saitama 337-8570,Japan
计算机与自动化
Current feedbackfault estimationiterative learning observerMarkov jump piecewise-affine system
《IEEE/CAA Journal of Automatica Sinica》 2024 (002)
P.418-429 / 12
supported in part by the National Natural Science Foundation of China (62222310, U1813201, 61973131, 62033008);the Research Fund for the Taishan Scholar Project of Shandong Province of China;the NSFSD(ZR2022ZD34);Japan Society for the Promotion of Science (21K04129);Fujian Outstanding Youth Science Fund (2020J06022)。
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