Vol. 128
Latest Volume
All Volumes
PIERC 142 [2024] PIERC 141 [2024] PIERC 140 [2024] PIERC 139 [2024] PIERC 138 [2023] PIERC 137 [2023] PIERC 136 [2023] PIERC 135 [2023] PIERC 134 [2023] PIERC 133 [2023] PIERC 132 [2023] PIERC 131 [2023] PIERC 130 [2023] PIERC 129 [2023] PIERC 128 [2023] PIERC 127 [2022] PIERC 126 [2022] PIERC 125 [2022] PIERC 124 [2022] PIERC 123 [2022] PIERC 122 [2022] PIERC 121 [2022] PIERC 120 [2022] PIERC 119 [2022] PIERC 118 [2022] PIERC 117 [2021] PIERC 116 [2021] PIERC 115 [2021] PIERC 114 [2021] PIERC 113 [2021] PIERC 112 [2021] PIERC 111 [2021] PIERC 110 [2021] PIERC 109 [2021] PIERC 108 [2021] PIERC 107 [2021] PIERC 106 [2020] PIERC 105 [2020] PIERC 104 [2020] PIERC 103 [2020] PIERC 102 [2020] PIERC 101 [2020] PIERC 100 [2020] PIERC 99 [2020] PIERC 98 [2020] PIERC 97 [2019] PIERC 96 [2019] PIERC 95 [2019] PIERC 94 [2019] PIERC 93 [2019] PIERC 92 [2019] PIERC 91 [2019] PIERC 90 [2019] PIERC 89 [2019] PIERC 88 [2018] PIERC 87 [2018] PIERC 86 [2018] PIERC 85 [2018] PIERC 84 [2018] PIERC 83 [2018] PIERC 82 [2018] PIERC 81 [2018] PIERC 80 [2018] PIERC 79 [2017] PIERC 78 [2017] PIERC 77 [2017] PIERC 76 [2017] PIERC 75 [2017] PIERC 74 [2017] PIERC 73 [2017] PIERC 72 [2017] PIERC 71 [2017] PIERC 70 [2016] PIERC 69 [2016] PIERC 68 [2016] PIERC 67 [2016] PIERC 66 [2016] PIERC 65 [2016] PIERC 64 [2016] PIERC 63 [2016] PIERC 62 [2016] PIERC 61 [2016] PIERC 60 [2015] PIERC 59 [2015] PIERC 58 [2015] PIERC 57 [2015] PIERC 56 [2015] PIERC 55 [2014] PIERC 54 [2014] PIERC 53 [2014] PIERC 52 [2014] PIERC 51 [2014] PIERC 50 [2014] PIERC 49 [2014] PIERC 48 [2014] PIERC 47 [2014] PIERC 46 [2014] PIERC 45 [2013] PIERC 44 [2013] PIERC 43 [2013] PIERC 42 [2013] PIERC 41 [2013] PIERC 40 [2013] PIERC 39 [2013] PIERC 38 [2013] PIERC 37 [2013] PIERC 36 [2013] PIERC 35 [2013] PIERC 34 [2013] PIERC 33 [2012] PIERC 32 [2012] PIERC 31 [2012] PIERC 30 [2012] PIERC 29 [2012] PIERC 28 [2012] PIERC 27 [2012] PIERC 26 [2012] PIERC 25 [2012] PIERC 24 [2011] PIERC 23 [2011] PIERC 22 [2011] PIERC 21 [2011] PIERC 20 [2011] PIERC 19 [2011] PIERC 18 [2011] PIERC 17 [2010] PIERC 16 [2010] PIERC 15 [2010] PIERC 14 [2010] PIERC 13 [2010] PIERC 12 [2010] PIERC 11 [2009] PIERC 10 [2009] PIERC 9 [2009] PIERC 8 [2009] PIERC 7 [2009] PIERC 6 [2009] PIERC 5 [2008] PIERC 4 [2008] PIERC 3 [2008] PIERC 2 [2008] PIERC 1 [2008]
2022-12-22
Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Generator Based on Online Least Squares Support Vector Machine Inverse System and Internal Model Controllers
By
Progress In Electromagnetics Research C, Vol. 128, 1-15, 2023
Abstract
An outer rotor coreless bearingless permanent magnet synchronous generator (ORC-BPMSG) is a multivariable, nonlinear, and strongly coupled system. In order to realize the precise control of the ORC-BPMSG, a decoupling control strategy based on online least squares support vector machine (OLS-SVM) inverse system and internal model controllers is proposed. Firstly, on the basis of introducing its operation principle, the mathematical model is established. Secondly, on the basis of analyzing its reversibility, a real-time inverse system of ORC-BPMSG is obtained by using OLS-SVM, and it is connected in series with the original system to form a pseudo-linear system, which realizes the linearization and decoupling of the ORC-BPMSG. Thirdly, the internal model controller is designed to perform closed-loop control of the pseudo-linear system. Finally, the simulated and experimental results show that the proposed control strategy has better stability and decoupling performance than the decoupling control strategy based on the LS-SVM inverse system and PID (Proportion Integral Derivative).
Citation
Huangqiu Zhu, and Liangyu Shen, "Decoupling Control of Outer Rotor Coreless Bearingless Permanent Magnet Synchronous Generator Based on Online Least Squares Support Vector Machine Inverse System and Internal Model Controllers," Progress In Electromagnetics Research C, Vol. 128, 1-15, 2023.
doi:10.2528/PIERC22102105
References

1. Xu, Q., S. Yuan, and X. Liu, "Online detection and location of eccentricity fault in PMSG with external magnetic sensing," IEEE Transactions on Industrial Electronics, Vol. 69, No. 10, 9749-9760, 2022.
doi:10.1109/TIE.2022.3159947

2. Zhang, X., Y. Li, and K. Wang, "Model predictive control of the open-winding PMSG system based on three-dimensional reference voltage-vector," IEEE Transactions on Industrial Electronics, Vol. 67, No. 8, 6312-6322, 2020.
doi:10.1109/TIE.2019.2938478

3. Nian, H. and L. Chen, "Control techniques of open winding PMSG systems fed by integration of three level NPC converters and diode bridges," Chinese Society for Electrical Engineering, Vol. 36, No. 22, 6238-6245, 2016.

4. Koczara, W. and E. Emest, "Smart and decoupled power electronic generation system," Proceeding of IEEE Power Electronics Specialists Conference, 1902-1907, 2004.

5. Ooshima, M., S. Kitazawa, A. Chiba, et al. "Design and analyses of a coreless-stator-type bearingless motor/generator for clean energy generation and storage systems," IEEE Transactions on Magnetics, Vol. 42, No. 10, 3461-3463, 2006.
doi:10.1109/TMAG.2006.879071

6. Ooshima, M., S. Kobayashi, and H. Tanaka, "Magnetic suspension performance of a bearingless motor/generator for flywheel energy storage systems," Proceeding of IEEE PES General Meeting, 1-4, 2010.

7. Diao, X., Y. Hu, H. Zhu, et al. "Bearingless permanent magnet synchronous generator levitation force and electricity generation performance under variable speed and load situation," Journal of Electrical Machinery and Control, Vol. 21, No. 9, 63-72, 2017.

8. Hua, Y., H. Zhu, and Y. Xu, "Multi-objective optimization design of bearingless permanent magnet synchronous generator," IEEE Transactions on Applied Superconductivity, Vol. 30, No. 4, 1-5, 2020.
doi:10.1109/TASC.2020.2970661

9. Liu, B., Y. Zhang, and X. Yan, "Internal model control of doubly fed induction generators based on inverse system method," Power System Technology, Vol. 35, No. 4, 149-153, 2011.

10. Sun, X., L. Chen, H. Jiang, et al. "High-performance control for a bearingless permanent-magnet synchronous motor using neural network inverse scheme plus internal model controllers," IEEE Transactions on Industrial Electronics, Vol. 6, No. 63, 3479-3488, 2016.
doi:10.1109/TIE.2016.2530040

11. Gu, Z. and H. Zhu, "Active disturbance rejection control of 5-degree-of freedom bearingless permanent magnet synchronous motor based on fuzzy neural network inverse system," ISA Transactions, Vol. 101, 1-14, 2020.
doi:10.1016/j.isatra.2019.09.021

12. Zhu, H., L. Cao, Y. Li, et al. "Decoupling control of 5-degree of freedom bearingless synchronous reluctance motor based on least square support vector machine inverse system," Chinese Society for Electrical Engineering, Vol. 33, No. 15, 99-108, 2013.

13. Liu, G., Y. Zhang, H. Wei, et al. "Least squares support vector machines inverse control for two-motor variable frequency speed-regulating system based on active disturbances rejection," Chinese Society for Electrical Engineering, Vol. 32, No. 6, 138-144, 2012.

14. Liu, G., L. Chen, W. Zhao, et al. "Internal model control of permanent magnet synchronous motor using support vector machine generalized inverse," IEEE Transactions on Industrial Informatics, Vol. 9, No. 2, 890-899, 2013.
doi:10.1109/TII.2012.2222652

15. Xing, J., R. Wang, Q. Yang, et al. "Online training algorithm research based on improved weighed LSSVM," Proceeding of Chinese Control Conference, 5055-5060, 2010.

16. Liu, B. and X. Cheng, "An incremental algorithm of support vector machine based on distance and K nearest neighbor," Proceeding of IEEE International Conference on Computer Science and Automation Engineering, 18-20, 2011.

17. Wong, P., Q. Xu, C. Vong, et al. "Rate-dependent hysteresis modeling and control of a piezo stage using online support vector machine and relevance vector machine," IEEE Transactions on Industrial Electronics, Vol. 59, No. 4, 1988-2001, 2011.
doi:10.1109/TIE.2011.2166235

18. Xu, B. and H. Zhu, "The parameters of LS-SVM are optimized by improved genetic algorithm and improved particle swarm optimization algorithm to improve the performance of LS-SVM, thus improving the fitting accuracy of the inverse system," IEEE Transactions on Industrial Electronics, Vol. 69, No. 12, 12182-12190, 2022.
doi:10.1109/TIE.2021.3130345

19. Zhu, H. and T. Liu, "Rotor displacement self-sensing modeling of six-pole radial hybrid magnetic bearing using improved particle swarm optimization support vector machine," IEEE Transactions on Industrial Electronics, Vol. 35, No. 11, 12296-12306, 2020.

20. Hu, J., M. Wu, X. Chen, et al. "A multilevel prediction model of carbon efficiency based on the differential evolution algorithm for the iron ore sintering process," IEEE Transactions on Industrial Electronics, Vol. 65, No. 11, 8778-8787, 2018.
doi:10.1109/TIE.2018.2811371