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Finite-Time Prescribed Performance-Based Adaptive Fuzzy Command Filtering Control for Permanent Magnet Synchronous Motors with Actuator Faults

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Abstract

This research focuses on the issue of adaptive fuzzy fault-tolerant position tracking for permanent magnet synchronous motors (PMSMs) subject to finite-time prescribed performance. An improved finite-time prescribed performance control strategy, in which unknown nonlinear functions can be tackled via fuzzy logic systems (FLSs), is presented via incorporating the approach of prescribed performance control with the technique of command filter. In addition, the command filtered method is utilized to conquer the ‘explosion of complexity’ emerged in the classic backstepping method and the error compensation mechanism is adopted to diminish the error generated by filtering process. Further, the impact of actuator failures is dealt with based on fault-tolerant control. It is proven that the designed controllers not only assure the semi-global boundedness of all the controlled system signals, but also make the output tracking error is preserved in a specified prescribed performance within a finite-time interval. Finally, simulation results are supplied to display the significance and potential of the proposed control technique.

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References

  1. Wu, L., Zheng, W.X., Gao, H.: Dissipativity-based sliding mode control of switched stochastic systems. IEEE Trans. Autom. Control 58(3), 785–791 (2013)

    Article  MathSciNet  Google Scholar 

  2. Barambones, O., Alkorta, P.: Position control of the induction motor using an adaptive sliding-mode controller and observers. IEEE Trans. Industrial Electron. 61(12), 6556–6565 (2014)

    Article  Google Scholar 

  3. Wei, Y., Chen, Y., Liang, S., Wang, Y.: A novel algorithm on adaptive backstepping control of fractional order systems. Neurocomputing 165, 395–402 (2015)

    Article  Google Scholar 

  4. Zhou, J., Wen, C., Wang, W., Yang, F.: Adaptive backstepping control of nonlinear uncertain systems with quantized states. IEEE Trans. Autom. Control 64(11), 4756–4763 (2019)

    Article  MathSciNet  Google Scholar 

  5. Zhou, S., Feng, G., Feng, C.B.: Robust control for a class of uncertain nonlinear systems: adaptive fuzzy approach based on backstepping. Fuzzy Sets Syst. 151(1), 1–20 (2005)

    Article  MathSciNet  Google Scholar 

  6. Xing, L., Wen, C., Su, H., Liu, Z., Cai, J.: Robust control for a class of uncertain nonlinear systems with input quantization. Int. J. Robust Nonlinear Control 26(8), 1585–1596 (2016)

    Article  MathSciNet  Google Scholar 

  7. Yu, J., Chen, B., Yu, H., Gao, J.: Adaptive fuzzy tracking control for the chaotic permanent magnet synchronous motor drive system via backstepping. Nonlinear Anal.: Real World Appl. 12(1), 671–681 (2011)

    Article  MathSciNet  Google Scholar 

  8. Yu, J., Shi, P., Dong, W., Chen, B., Lin, C.: Neural network-based adaptive dynamic surface control for permanent magnet synchronous motors. IEEE Trans. Neural Netw. Learn. Syst. 26(3), 640–645 (2015)

    Article  MathSciNet  Google Scholar 

  9. Yang, X., Yu, J., Wang, Q.G., Zhao, L., Yu, H., Lin, C.: Adaptive fuzzy finite-time command filtered tracking control for permanent magnet synchronous motors. Neurocomputing 337, 110–119 (2019)

    Article  Google Scholar 

  10. Lu, S., Wang, X., Li, Y.: Adaptive neural network finite-time command filtered tracking control of fractional-order permanent magnet synchronous motor with input saturation. J. Franklin Inst. 357(18), 13707–13733 (2020)

    Article  MathSciNet  Google Scholar 

  11. Zou, M., Yu, J., Ma, Y., Zhao, L., Lin, C.: Command filtering-based adaptive fuzzy control for permanent magnet synchronous motors with full-state constraints. Inf. Sci. 518, 1–12 (2020)

    Article  MathSciNet  Google Scholar 

  12. Ding, L., Wang, W., Yu, Y.: Finite-time adaptive NN control for permanent magnet synchronous motors with full-state constraints. Neurocomputing 449, 435–442 (2021)

    Article  Google Scholar 

  13. Yue, H., Wang, H., Wang, Y.: Adaptive fuzzy fixed-time tracking control for permanent magnet synchronous motor. Int. J. Robust Nonlinear Control 32(5), 3078–3095 (2022)

    Article  MathSciNet  Google Scholar 

  14. Ma, L., Zhu, F., Zhao, X.: Human-in-the-loop consensus control for multiagent systems with external disturbances. IEEE Trans. Neural Netw. Learn. Syst. (2023). https://doi.org/10.1109/TNNLS.2023.3246567

    Article  Google Scholar 

  15. Ma, L., Zhu, F., Zhao, X.: Human-in-the-loop formation-containment control for multiagent systems: an observer-based distributed unknown input reconstruction method. IEEE Trans. Neural Netw. Learn. Syst. (2023). https://doi.org/10.1109/TCNS.2023.3269010

    Article  Google Scholar 

  16. Cui, D., Ahn, C.K., Xiang, Z.: Fault-tolerant fuzzy observer-based fixed-time tracking control for nonlinear switched systems. IEEE Trans. Fuzzy Syst. (2023). https://doi.org/10.1109/TFUZZ.2023.3284917

    Article  Google Scholar 

  17. Cui, D., Zou, W., Guo, J., Xiang, Z.: Adaptive fault-tolerant decentralized tracking control of switched stochastic uncertain nonlinear systems with time-varying delay. Int. J. Adaptive Control Signal Process. 36(12), 2971–2987 (2022)

    Article  MathSciNet  Google Scholar 

  18. Zhang, T.P., Ge, S.S.: Adaptive dynamic surface control of nonlinear systems with unknown dead zone in pure feedback form. Automatica 44(7), 1895–1903 (2008)

    Article  MathSciNet  Google Scholar 

  19. Liu, H., Pan, Y., Cao, J.: Composite learning adaptive dynamic surface control of fractional-order nonlinear systems. IEEE Trans. Cybern. 50(6), 2557–2567 (2019)

    Article  Google Scholar 

  20. Ma, H., Liang, H., Zhou, Q., Ahn, C.K.: Adaptive dynamic surface control design for uncertain nonlinear strict-feedback systems with unknown control direction and disturbances. IEEE Trans. Syst. Man Cybern.: Syst. 49(3), 506–515 (2018)

    Article  Google Scholar 

  21. Ling, S., Wang, H., Liu, P.X.: Adaptive fuzzy dynamic surface control of flexible-joint robot systems with input saturation. IEEE/CAA J. Automatica Sinica 6(1), 97–107 (2019)

    Article  MathSciNet  Google Scholar 

  22. Farrell, J.A., Polycarpou, M., Sharma, M., Dong, W.: Command filtered backstepping. IEEE Trans. Autom. Control 54(6), 1391–1395 (2009)

    Article  MathSciNet  Google Scholar 

  23. Dong, W., Farrell, J.A., Polycarpou, M.M., Djapic, V., Sharma, M.: Command filtered adaptive backstepping. IEEE Trans. Control Syst. Technol. 20(3), 566–580 (2012)

    Article  Google Scholar 

  24. Zhang, H., Liu, Y., Dai, J., Wang, Y.: Command filtered backstepping, Command filter based adaptive fuzzy finite-time control for a class of uncertain nonlinear systems with hysteresis. IEEE Trans. Fuzzy Syst. 29(9), 2553–2564 (2019)

    Article  Google Scholar 

  25. Ling, S., Wang, H., Liu, P.X.: Adaptive fuzzy tracking control of flexible-joint robots based on command filtering. IEEE Trans. Ind. Electron. 67(5), 4046–4055 (2020)

    Article  Google Scholar 

  26. Xin, C., Li, Y.X., Ahn, C.K.: Adaptive neural asymptotic tracking of uncertain non-strict feedback systems with full-state constraints via command filtered technique. IEEE Tran. Neural Netw. Learn. Syst. (2022). https://doi.org/10.1109/TNNLS.2022.3141091

    Article  Google Scholar 

  27. Wang, H., Kang, S., Zhao, X., Xu, N., Li, T.: Command filter-based adaptive neural control design for nonstrict-feedback nonlinear systems with multiple actuator constraints. IEEE Trans. Cybern. 52(11), 12561–12570 (2022)

    Article  Google Scholar 

  28. Kang, S., Liu, P.X., Wang, H.: Finite-time command filter-based adaptive fuzzy tracking control for stochastic nonlinear induction motors systems with unknown backlash-like hysteresis. J. Franklin Inst. 359(15), 7936–7960 (2022)

    Article  MathSciNet  Google Scholar 

  29. Wang, H., Kang, S., Feng, Z.: Finite-time adaptive fuzzy command filtered backstepping control for a class of nonlinear systems. Int. J. Fuzzy Syst. 21, 2575–2587 (2019)

    Article  MathSciNet  Google Scholar 

  30. Bechlioulis C. P., Rovithakis G. A.: Prescribed performance adaptive control of SISO feedback linearizable systems with disturbance. In: 16th Mediterranean Conference on Control and Automation. 1035-1040 (2008)

  31. Xia, X., Zhang, T., Yi, Y., Shen, Q.: Adaptive prescribed performance control of output feedback systems including input unmodeled dynamics. Neurocomputing 190, 226–236 (2016)

    Article  Google Scholar 

  32. Cui, G., Yu, J., Shi, P.: Observer-based finite-time adaptive fuzzy control with prescribed performance for nonstrict-feedback nonlinear systems. IEEE Trans. Fuzzy Syst. 30(3), 767–778 (2020)

    Article  Google Scholar 

  33. Ma, H., Zhou, Q., Li, H., Lu, R.: Adaptive prescribed performance control of a flexible-joint robotic manipulator with dynamic uncertainties. IEEE Trans. Cybern. 52(12), 12905–12915 (2022)

    Article  Google Scholar 

  34. Sun, W., Su, S.F., Xia, J., Zhuang, G.: Command filter-based adaptive prescribed performance tracking control for stochastic uncertain nonlinear systems. IEEE Trans. Syst. Man Cybern.: Syst. 51(10), 6555–6563 (2021)

    Article  Google Scholar 

  35. Shao, X., Tong, S.: Adaptive prescribed performance decentralized control for stochastic nonlinear large-scale systems. Int. J. Adaptive Control Signal Process. 32(12), 1782–1800 (2018)

    Article  MathSciNet  Google Scholar 

  36. Liu, Y., Liu, X., Jing, Y., Zhang, Z.: A novel finite-time adaptive fuzzy tracking control scheme for nonstrict feedback systems. IEEE Trans. Fuzzy Syst. 27(4), 646–658 (2019)

    Article  Google Scholar 

  37. Sui, S., Tong, S.: Finite-time fuzzy adaptive PPC for nonstrict-feedback nonlinear MIMO systems. IEEE Transactions on Cybernetics. 53(2), 732–742 (2023)

    Article  Google Scholar 

  38. Liu, Y., Liu, X., Jing, Y.: Adaptive neural networks finite-time tracking control for non-strict feedback systems via prescribed performance. Inf. Sci. 468, 29–46 (2018)

    Article  MathSciNet  Google Scholar 

  39. Chen, M., Tao, G.: Adaptive fault-tolerant control of uncertain nonlinear large-scale systems with unknown dead zone. IEEE Trans. Cybern. 48(6), 1851–1862 (2016)

    Article  Google Scholar 

  40. Cui, D., Niu, B., Wang, H., Yang, D.: Adaptive fuzzy output-feedback fault-tolerant tracking control of a class of uncertain nonlinear switched systems. Int. J. Syst. Sci. 50(14), 2673–2686 (2019)

    Article  MathSciNet  Google Scholar 

  41. Bai, W., Wang, H.: Robust adaptive fault-tolerant tracking control for a class of high-order nonlinear system with finite-time prescribed performance. Int. J. Robust Nonlinear Control 30(12), 4708–4725 (2020)

    Article  MathSciNet  Google Scholar 

  42. Wu, C., Liu, J., Xiong, Y., Wu, L.: Observer-based adaptive fault-tolerant tracking control of nonlinear nonstrict-feedback systems. IEEE Trans. Neural Netw. Learn. Syst. 29(7), 3022–3033 (2018)

    MathSciNet  Google Scholar 

  43. Li, Y.X., Yang, G.H.: Adaptive fuzzy decentralized control for a class of large-scale nonlinear systems with actuator faults and unknown dead zones. IEEE Trans. Syst. Man Cybern.: Syst. 45(7), 1–12 (2017)

    Google Scholar 

  44. Wang, H., Shen, L., Wang, D., Niu, B., Zhao, X.: Fast finite-time adaptive neural fault-tolerant tracking control for multi-input multi-output systems with full-state constraints. Int. J. Adaptive Control Signal Process. 36(9), 2269–2288 (2021)

    Article  MathSciNet  Google Scholar 

  45. Bai, W., Liu, P.X., Wang, H., Chen, M.: Adaptive finite-time control for nonlinear multi-agent high-order systems with actuator faults. Int. J. Syst. Sci. 53(11), 2437–2460 (2022)

    Article  MathSciNet  Google Scholar 

  46. Xu, Y., Tong, S., Li, Y.: Prescribed performance fuzzy adaptive fault-tolerant control of non-linear systems with actuator faults. IET Control Theory Appl. 8(6), 420–431 (2014)

    Article  MathSciNet  Google Scholar 

  47. Han, Y., Yu, J., Zhao, L., Yu, H., Lin, C.: Finite-time adaptive fuzzy control for induction motors with input saturation based on command filtering. IET Control Theory Appl. 12(15), 2148–2155 (2018)

    Article  MathSciNet  Google Scholar 

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Authors and Affiliations

  1. School of Mechanical, Electronic and Control Engineering, Beijing Jiaotong University, Beijing, 100044, China

    Shijia Kang

  2. Department of Systems and Computer Engineering, Carleton University, Ottawa, ON, K1S 5B6, Canada

    Peter Xiaoping Liu

  3. College of Mathematical Sciences, Bohai University, Jinzhou, 121000, China

    Huanqing Wang

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  1. Shijia Kang
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  2. Peter Xiaoping Liu
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Correspondence to Shijia Kang.

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Kang, S., Liu, P.X. & Wang, H. Finite-Time Prescribed Performance-Based Adaptive Fuzzy Command Filtering Control for Permanent Magnet Synchronous Motors with Actuator Faults. Int. J. Fuzzy Syst. (2024). https://doi.org/10.1007/s40815-024-01705-3

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  • DOI: https://doi.org/10.1007/s40815-024-01705-3

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