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Full State Constraints-based Adaptive Fuzzy Finite-time Command Filtered Control for Permanent Magnet Synchronous Motor Stochastic Systems

  • Control Theory and Applications
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Abstract

In this paper, the problem of full state constraints-based adaptive fuzzy finite-time command filtered control (FTCFC) is investigated for permanent magnet synchronous motor (PMSM) stochastic systems. Firstly, the barrier Lyapunov functions are introduced to avoid the violation of the state constraints. Then, the unknown nonlinear functions are approximated by the fuzzy logic systems (FLSs) in PMSM stochastic systems. Furthermore, the finite-time control method is proposed by combing command filtered backstepping and adaptive fuzzy control, and the improved finite-time command filter can solve the problem of “explosion of complexity”, and relax the condition of input signal. It is shown that the required tracking performance can be realized in finite time, and the boundedness of all signals in the closed-loop system is guaranteed. Finally, simulation results illustrate the validity of the studied method.

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Author information

Authors and Affiliations

  1. College of Automation, Qingdao University, Qingdao, 266071, China

    Qi Jiang, Yumei Ma, Jiapeng Liu & Jinpeng Yu

Authors
  1. Qi Jiang
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  2. Yumei Ma
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  3. Jiapeng Liu
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  4. Jinpeng Yu
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Corresponding author

Correspondence to Jinpeng Yu.

Additional information

This work was supported by the National Key Research and Development Plan (2017YFB1303503), the National Natural Science Foundation of China (61973179), the Taishan Scholar Special Project Fund (TSQN20161026), and the Qingdao key research and development special project (21-1-2-6-nsh).

Qi Jiang received his B.Sc. degree in automation from Qingdao University, Qingdao, China, in 2019. He is currently working toward an M.Sc. degree in control science and engineering, Qingdao University, Qingdao, China. His research interests include motor control, applied nonlinear control, and intelligent systems.

Yumei Ma received her B.Sc. degree in computer science and technology from Shandong University, Jinan, China, in 2002, an M.Sc. degree in computer application technology from Shandong University, Jinan, China, in 2006, and a Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2014. She is currently a Lecturer at the College of Computer Science Technology, Qingdao University. Her research interests include nonlinear signal processing and weak signal detection.

Jiapeng Liu received his M.Sc. degree in control science and engineering, Qingdao University, Qingdao, China, in 2015 and a Ph.D. degree of engineering in Shandong University, Jinan, China, in 2019. He is currently a Distinguished Professor at the School of Automation, Qingdao University. His research interests include electrical energy conversion, ejector refrigeration, and model predictive control.

Jinpeng Yu received his B.Sc. degree in automation from Qingdao University, Qingdao, China, in 2002, an M.Sc. degree in system engineering from Shandong University, Jinan, China, in 2006, and a Ph.D. degree from the Institute of Complexity Science, Qingdao University, Qingdao, China, in 2011. He is currently a Distinguished Professor at the School of Automation and Electrical Engineering, Qingdao University. He is a recipient of the Shandong Province Taishan Scholar Special Project Fund and Shandong Province Fund for Outstanding Young Scholars. His research interests include electrical energy conversion and motor control, applied nonlinear control, and intelligent systems.

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Jiang, Q., Ma, Y., Liu, J. et al. Full State Constraints-based Adaptive Fuzzy Finite-time Command Filtered Control for Permanent Magnet Synchronous Motor Stochastic Systems. Int. J. Control Autom. Syst. 20, 2543–2553 (2022). https://doi.org/10.1007/s12555-021-0558-2

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  • DOI: https://doi.org/10.1007/s12555-021-0558-2

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