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Characteristic Model-based Adaptive Control with Genetic Algorithm Estimators for Four-PMSM Synchronization System

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  • Intelligent Control and Applications
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Abstract

For four-PMSM synchronization systems, the complex structure, nonlinearities and uncertainties make accurate modeling and precise control difficult to realize. This article proposes an easily realized adaptive control scheme for four-PMSM synchronization system. This control scheme combines novel characteristic modeling, genetic algorithm and terminal sliding mode control together. Firstly, the four-PMSM synchronization system is described by characteristic model, which significantly simplifies system’s dynamic model without losing its high-order characteristics. Secondly, a genetic algorithm estimator is newly designed to estimate the real-time value of parameters in characteristic model, which is the key issue in obtaining an accurate system model. Thirdly, an adaptive discrete-time terminal sliding mode controller is proposed based on the characteristic model to enhance system robustness. Also, this controller can eliminate chattering effect when system is reversing. Then, the stability of the closed-loop system is proved by Lyapunov stability theorem. Finally, simulation and experiment results verify the adaptiveness and robustness of the proposed control scheme.

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References

  1. M. A. Ali, C. Kim, S. Kim, A. M. Khan, J. Iqbal, M. Z. Khalil, D. Lim, and C. Han, “Lateral acceleration potential field function control for rollover safety of multi-wheel military vehicle with in-wheel-motors,” International Journal of Control, Automation and Systems, vol. 15, no. 2, pp. 8376–847, April 2017.

    Article  Google Scholar 

  2. F. Claveau, P. Chevrel, and K. Knittel, “A 2DOF gainscheduled controller design methodology for a multi-motor web transport system,” Control Engineering Practice, vol. 16, no. 5, pp. 6096–622, May 2008.

    Article  Google Scholar 

  3. W. Boukadida, A. Benamor, H. Messaoud, and P. Siarry, “Metaheuristics-based multi-objective design of global robust optimal sliding mode control of discrete uncertain systems,” International Journal of Control, Automation, and Systems, vol. 17, no. 6, pp. 13786–1392, 2019.

    Article  Google Scholar 

  4. E. Levi, M. Jones, S. N. Vukosavic, and H. A. Toliyat, “A novel concept of a multiphase, multimotor vector controlled drive system supplied from a single voltage source inverter,” IEEE Trans. on Power Electronics, vol. 19, no. 2, pp. 3206–335, April 2004.

    Article  Google Scholar 

  5. H. Kaghazchi and J. Mannion, “Synchronisation of a multimotor speed control system,” Assembly Automation, vol. 33, no. 4, pp. 3746–385, April 2013.

    Article  Google Scholar 

  6. H. H. Lee and U. H. Jeong, “A study on speed synchronization for multi-motors using controller area network,” Proc. of the 4th Korea-Russia International Symposium Conf. on Science and Technology, February 2000.

    Google Scholar 

  7. B. Jeftenic, M. Bebic, and S. Statkic, “Controlled multimotor drives,” International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 13926–1398, June 2006.

    Google Scholar 

  8. Z. Ping, Q. Ma, T. Wang, Y. Huang, and J.-G. Lu, “Speed tracking control of permanent magnet synchronous motor by a novel two-step internal model control approach” International Journal of Control, Automation, and Systems, vol. 16, no. 6, pp. 27546–2762, 2018.

    Article  Google Scholar 

  9. A. Jimenez-Fernandez, G. Jimenez-Moreno, A. Linares-Barranco, M. J. Dominguez-Morales, R. Paz-Vicente, and A. Civit-Balcells, “A Neuro-inspired spike-based PID motor controller for multi-motor robots with low cost FPGAs,” Sensors, vol. 12, no. 4, pp. 38316–3856, April 2012.

    Article  Google Scholar 

  10. T. Shi, H. Liu, Q. Geng, and C. Xia, “Improved relative coupling control structure for multi-motor speed synchronous driving system,” IET Electric Power Applications, vol. 10, no. 6, pp. 4516–457, March 2016.

    Article  Google Scholar 

  11. F. Beltran-Carbajal, R. Tapia-Olvera, I. Lopez-Garcia, A. Valderrabano-GonzalezJulio, J. C. Rosas-CaroJose, and J. L. Hernandez-Avila, “Extended PI feedback tracking control for synchronous motors,” International Journal of Control, Automation, and Systems, vol. 17, no. 6, pp. 13466–1358, 2019.

    Article  Google Scholar 

  12. S. Wu and J. Zhang, “A terminal sliding mode observer based robust backstepping sensorless speed control for interior permanent magnet synchronous motor,” International Journal of Control, Automation, and Systems, vol. 16, no. 6, pp. 27436–2753, 2018.

    Google Scholar 

  13. M. Mengoni, A. Tani, L. Zarri, G. Serra, and D. Casadei, “Position control of a multi-motor drive based on seriesconnected five-phase tubular PM actuators,” IEEE Trans. on Industry Applications, vol. 48, no. 6, pp. 20486–2058, November 2012.

    Article  Google Scholar 

  14. J. Li, X. Liu, C. Chong, and L. Guohai, “Application of an adaptive controller with a single neuron in control of multimotor synchronous system,” IEEE International Conference on Industrial Technology, April 2008.

    Google Scholar 

  15. K. Yue, Q. Chen, G. Yuan, Z. Han, and M. Li, “Leaderfollowing control of multi-motor systems in directed networks with fixed topologies under network environment,” Computer Engineering & Applications, vol. 51, no. 7, pp. 2586–261, July 2015.

    Google Scholar 

  16. H. Wu, J. Hu, and Y. Xie, Characteristic Model-based Intelligent Adaptive Control, China Science and Technology Press, Beijing, China, 2009.

    Google Scholar 

  17. H. Wu, J. Hu, and Y. Xie, “Characteristic model-based allcoefficient adaptive control method and its applications,” IEEE Trans. on Systems, Man & Cybernetics Part C, vol. 37, no. 2, pp. 2136–221, March 2007.

    Google Scholar 

  18. X. Wang, Y. F. Wu, E. Zhang, J. Guo, and Q. Chen, “Characteristic model-based adaptive controller with discrete extended state observer for servo systems,” Proc. Inst. Mech., vol. 231, no. 4, pp. 2596–270, 2017.

    Google Scholar 

  19. Y. F. Wu, “Research and implementation of a kind of servo systems driven by multi motors synchronously based on characteristic modeling,” P67–72, 2014.

    Google Scholar 

  20. X. Wang, Y. Wu, E. Zhang, J. Guo, and Q. Chen, “Adaptive terminal sliding-mode controller based on characteristic model for gear transmission servo systems,” Trans. of the Institute of Measurement and Control, vol. 41, no. 1, pp. 2196–234, January 2019.

    Google Scholar 

  21. Y. Chang, T. Jiang, and Z. Pu, “Adaptive control of hypersonic vehicles based on characteristic models with fuzzy neural network estimators,” Aerospace Science and Technology, vol. 68, pp. 4756–485, September 2017.

    Article  Google Scholar 

  22. X. Deng, P. Jiang, X. Peng, and C. Mi, “An intelligent outlier detection method with one class support tucker machine and genetic algorithm towards big sensor data in internet of things,” IEEE Trans. on Industrial Electronics, vol. 66, no. 6, pp. 46726–4683, August 2018.

    Google Scholar 

  23. M. Afzalirad and M. Shafipour, “Design of an efficient genetic algorithm for resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions,” Journal of Intelligent Manufacturing, vol. 29, no. 2, pp. 4236–437, February 2018.

    Article  Google Scholar 

  24. L. Zhao, J. Yu, C. Lin, and H. Yu, “Distributed adaptive fixed-time consensus tracking for second-order multi-agent systems using modified terminal sliding mode,” Applied Mathematics & Computation, vol. 312, pp. 236–35, November 2017.

    Article  MathSciNet  Google Scholar 

  25. H. Du, X. Chen, G. Wen, X. Yu, and J. Liu, “Discrete-time fast terminal sliding mode control for permanent magnet linear motor,” IEEE Trans. on Industrial Electronics, vol. 65, no. 12, pp. 99166–9927, Dec. 2018.

    Google Scholar 

  26. S. Li, H. Du, and X. Yu, “Discrete-time terminal sliding mode control systems based on Euler’s discretization,” IEEE Trans. on Automatic Control, vol. 59, no. 2, pp. 5466–552, February 2014.

    Article  MathSciNet  Google Scholar 

  27. J. Xie, S. Zhao, Z. Sha, and L. Jintao, “Position servo control of the slider in double toggle mechanical press using Bezier curve model and fuzzy control,” Proc. of International Conference on IEEE Trans. Autom. Sci. Eng., August. 2011.

    Google Scholar 

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, 200 Xiaolingwei, XuanWu Block, Nanjing, Jiangsu Province, China

    Yang Gao, Yifei Wu, Xiang Wang & Qingwei Chen

Authors
  1. Yang Gao
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  2. Yifei Wu
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  3. Xiang Wang
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  4. Qingwei Chen
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Corresponding author

Correspondence to Yifei Wu.

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Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended by Associate Editor Kyeong-Hwa Kim under the direction of Editor Euntai Kim. This work is supported by the Natural Science Foundation of China (61673217, 61673214, 61673219 and 61333008) and Postgraduate Research &Practice Innovation Program of Jiangsu Province (KYCX19_0301, KYCX19_0299, KYCX19_0300).

Yang Gao is a Ph.D. student in School of Automation, Nanjing University of Science & Technology. His research interests include adaptive control, servo system, characteristic model methodology and system identification.

Yifei Wu is a Professor in School of Automation, Nanjing University of Science & Technology. His research interests include servo system control, intelligent robots and integrated navigation.

Xiang Wang is a Ph.D. student in School of Automation, Nanjing University of Science & Technology. His research interests include servo system control and characteristic modeling.

Qingwei Chen is a Professor in School of Automation, Nanjing University of Science & Technology. His research interests include servo system control, fuzzy control, integrated navigation and nonlinear system control.

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Gao, Y., Wu, Y., Wang, X. et al. Characteristic Model-based Adaptive Control with Genetic Algorithm Estimators for Four-PMSM Synchronization System. Int. J. Control Autom. Syst. 18, 1605–1616 (2020). https://doi.org/10.1007/s12555-019-0421-x

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  • DOI: https://doi.org/10.1007/s12555-019-0421-x

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