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Volume 11 Issue 2
Feb.  2024

IEEE/CAA Journal of Automatica Sinica

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Y. Zhu, N. Xu, F. Wu, X. Chen, and  D. Zhou,  “Fault estimation for a class of  Markov jump piecewise-affine systems: Current feedback based iterative learning approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 418–429, Feb. 2024. doi: 10.1109/JAS.2023.123990
Citation: Y. Zhu, N. Xu, F. Wu, X. Chen, and  D. Zhou,  “Fault estimation for a class of  Markov jump piecewise-affine systems: Current feedback based iterative learning approach,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 2, pp. 418–429, Feb. 2024. doi: 10.1109/JAS.2023.123990

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

doi: 10.1109/JAS.2023.123990
Funds:  This work was 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 and the NSFSD (ZR2022ZD34), Japan Society for the Promotion of Science (21K04129), and Fujian Outstanding Youth Science Fund (2020J06022)
More Information
  • In this paper, the issues of stochastic stability analysis and fault estimation are investigated for a class of continuous-time 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

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    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.

     

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    Highlights

    • The presented Markov jump PWA system contains both the Markov jump linear system and the ordinary PWA system as two special cases. The mode-dependent PWA iterative learning observer is developed to estimate both the augmented state and the non-differentiable actuator fault. With the increase of iteration numbers, the estimation error can be reduced gradually, and finally both the system state and the fault information can be estimated accurately
    • To ameliorate the iteration law to estimate faults rapidly with the minimum number of iterations, the current feedback mechanism is introduced into the iterative process, so that the designed iterative law includes both the results of the previous iteration based on the measurement output and the feedback information of the current iteration, where the effect of fault estimation errors on current iteration can be reduced by the feedback mechanism effectively
    • The mode-dependent and region-dependent Lyapunov function is constructed to adapt the stochastic stability with guaranteed H∞ performance. The investigated estimation error system illustrates the variations of switching model, the PWA regions, and the iteration process under the current feedback. Furthermore, the iterative relations between the plant information and the estimated information can be obtained

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