Decoupling Control of Bearingless Synchronous Reluctance Motor Based on SVM Inversely Optimized by ACO

Decoupling Control of Bearingless Synchronous Reluctance Motor Based on SVM Inversely Optimized by ACO

Xiaoyan Diao Huangqiu Zhu 

School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China

Corresponding Author Email: 
dxy@ujs.edu.cn; zhuhuangqiu@ujs.edu.cn
Page: 
216-228
|
DOI: 
https://doi.org/10.18280/mmc_a.900208
Received: 
25 May 2017
|
Accepted: 
3 June 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

Based on the SVM inversely optimized by ant colony optimization (ACO), this paper proposes a decoupling control approach for the bearingless synchronous reluctance motor (BSRM), a multivariable, nonlinear and strong-coupled system. Specifically, the inverse model approximated by SVM based on the ACO was cascaded with the original system to obtain three composite pseudo-linear subsystems, and the closed-loop controllers were designed for these pseudo-linear subsystems. The simulation results proved the effectiveness of the decoupling control strategy, and evidenced the dynamic performance and robustness of the control system.

Keywords: 

bearingless synchronous reluctance motor, support vector machine, ant colony optimization algorithm, decoupling control.

1. Introduction
2. Operation Principle and Mathematical Model of the BSRM
3. Decoupling Control Based on SVM Inversely Optimized by ACO
4. Simulation Research
5. Conclusion
Acknowledgments

This work was supported by the Key Development Program in Jiangsu Province under Project BE2016150, the Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (2014).

  References

1. G.H.B. Foo, X. Zhang, Robust Direct Torque Control of Synchronous Reluctance Motor Drives in the Field-Weakening Region, 2016, IEEE Trans. On Power Electronics, vol. 32, no. 2, pp. 1289-1298.

2. N. Boubaya, B. Saad, M. Maazouz, Radial active magnetic bearing control using fuzzy logic, 2016, Modelling, Measurement and Control A, vol. 89, no. 1, pp. 92-100. 

3. S.E. Saarakkala, M. Sokolov, M. Hinkkanen, J. Kataja, K. Tammi, State-Space Flux-Linkage Control of Bearingless Synchronous Reluctance Motors,2016, IEEE Energy Conversion Congress & Exposition (ECCE), Milwaukee, USA, no. 16671888.

4. V. Mukherjee, J. Pippuri, S.E. Saarakkala, Finite Element Analysis for Bearingless Operation of a Multi Flux Barrier Synchronous Reluctance Motor2015, International Conference on Electrical Machines and Systems, Pattaya, Thailand, pp. 688-691.

5. C. Michioka, T. Sakamoto, O. Ichikawa, A. Chiba, A decoupling control method of reluctance-type bearingless motors considering magnetic saturation, 1996, IEEE Trans. on Industry Applications., vol. 32, no. 5, pp. 1204-1210.

6. H. Zhang, H. Zhu, X. Diao, Feedback decoupling control of bearingless synchronous reluctance motor, 2007, Proc. of the 26th Chinese Control Conference, pp. 763-767.

7. L. Hertle, Hofmann W., Magnetic couplings in a bearingless reluctance machine, 2000, Proc. of the International Conference on Electrical Machines, vol. 16, no. 5, pp. 1776-1780.

8. T. Zhang, H. Zhu, Decoupling control based on inverse system for bearingless synchronous reluctance motor, 2011, Control Theory & Applications, vol. 28, no. 4, pp. 545-550.

9. X. Sun, L. Chen, Z. Yang, W. Zhang, High-Performance control for a bearingless permanent-magnet synchronous motor using neural network inverse scheme plus internal model controllers, 2016, IEEE Trans. on Industrial Electronics., vol. 63, no. 6, pp. 3479-3488.

10. X. Li, A. Zhang, X. Zhang, C. Li, L. Zhang, Rolling element bearing fault detection using support vector machine with improved ant colony optimization, 2013, Measurement, vol. 46, no. 8, pp. 2726-2734.

11. A. Chiba, T. Fukao, M. Rahman, An analysis of bearingless AC motors, 1994, IEEE Trans. On Energy Conversion, vol. 9, no. 1, pp. 61-68.

12. H. Zhu, Y. Li, L. Cao, Decoupling control of bearingless synchronous reluctance motor based on least squares support vector machines, 2012, Control and Decision, vol. 27, no. 11, pp. 1663-1668.

13. Q. Wu, L. Tang, H. Li, L. Ouyang, A co-evolutionary particle swarm optimization with dynamic topology for solving multi-objective optimization problems, 2016, Advances in Modelling and Analysis A, Vol. 53, No. 1, pp. 145-159.

14. X. Zhang, W. Chen, B. Wang, X. Chen, Intelligent fault diagnosis of rotating machinery using support vector machine with ant colony algorithm for synchronous feature selection and parameter optimization, 2015, Neurocomputing, vol. 167, pp. 260-279.