Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization

doi:10.1007/s12204-013-1376-3

上海交通大学学报（英文版） ›› 2013, Vol. 18 ›› Issue (2): 140-146.doi: 10.1007/s12204-013-1376-3

Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization

LI Xiang-bao* (李祥宝), JI Rui (季睿), YANG Yu-pu (杨煜普)

(Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China)

出版日期:2013-04-30 发布日期:2013-05-10
通讯作者: LI Xiang-bao (李祥宝) E-mail:xiangbao.li@gmail.com

Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization

LI Xiang-bao* (李祥宝), JI Rui (季睿), YANG Yu-pu (杨煜普)

(Department of Automation, Shanghai Jiaotong University, Shanghai 200240, China)

Online:2013-04-30 Published:2013-05-10
Contact: LI Xiang-bao (李祥宝) E-mail:xiangbao.li@gmail.com

摘要/Abstract

摘要： Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment — AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.

关键词: particle swarm optimization (PSO), PID controller, cryogenic ground support equipment (CGSE), cooperative random learning particle swarm optimization (CRPSO)

Abstract: Cryogenic ground support equipment (CGSE) is an important part of a famous particle physics experiment — AMS-02. In this paper a design method which optimizes PID parameters of CGSE control system via the particle swarm optimization (PSO) algorithm is presented. Firstly, an improved version of the original PSO, cooperative random learning particle swarm optimization (CRPSO), is put forward to enhance the performance of the conventional PSO. Secondly, the way of finding PID coefficient will be studied by using this algorithm. Finally, the experimental results and practical works demonstrate that the CRPSO-PID controller achieves a good performance.

Key words: particle swarm optimization (PSO), PID controller, cryogenic ground support equipment (CGSE), cooperative random learning particle swarm optimization (CRPSO)

中图分类号:

TP 273

LI Xiang-bao* (李祥宝), JI Rui (季睿), YANG Yu-pu (杨煜普). Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization[J]. 上海交通大学学报（英文版）, 2013, 18(2): 140-146.

LI Xiang-bao* (李祥宝), JI Rui (季睿), YANG Yu-pu (杨煜普). Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(2): 140-146.

参考文献

[1] AMS Collaboration. The Alpha Magnetic Spectrometer (AMS) on the International Space Station. Part I. Results from the test flight on the space shuttle [J]. Physics Reports-Review Section of Physics Letters, 2002, 366(6): 331-405.
[2] Yang Yu-pu, Li Xiang-bao, Ji-rui, et al. Control system design and test for cryogenic ground support equipment [J]. Journal of Shanghai Jiaotong University (Science), 2011, 16(5): 543-550.
[3] Ren Yu, Xu Xiao-bai. Optimization research of PSOPID algorithm for the design of brushless permanent magnet machines [C]//Fifth IEEE International Symposium on Embedded Computing. Washington, Piscataway, NJ: IEEE Press, 2008: 26-29.
[4] Zhang Yi, Yang Yu-pu. Application of a modified Smith predictor in a temperature-control system with time delay [J]. Process Automation Instrumentation, 2007, 28(02): 37-43 (in Chinese).
[5] Kennedy J, Eberhart R C. Particle swarm optimization [C]//Proceedings of IEEE International Conference on Neural Networks. Piscataway, NJ: IEEE Press, 1995: 1942-1948.
[6] Zhao Liang, Yang Yu-pu. PSO-based single multiplicative neuron model for time series prediction [J]. Expert Systems with Applications, 2009, 36: 2805-2812.
[7] Gaing Z L. A particle swarm optimization approach for optimum design of PID controller in AVR system [J]. IEEE Transactions on Energy Conversion, 2004, 9(2): 384-391.
[8] van den Bergh F, Engelbrecht A P. A study of particle swarm optimization particle trajectories [J]. Information Sciences, 2006, 176(8): 937-971.
[9] Wang Yu-jia, Yang Yu-pu. Particle swarm with equilibrium strategy of selection for multi-objective optimization [J]. Article European Journal of Operational Research, 2010, 200(1): 187-197.
[10] El-Abd M, Kamel M. Cooperative particle swarm optimizers: A powerful and promising approach [J]. Studies in Computational Intelligence, 2006, 31: 239-259.
[11] Baskar S., Suganthan P N. A novel concurrent particle swarm optimization [C]//Proceedings of IEEE Congress on Evolutionary Computation. Piscataway, NJ: IEEE Press, 2004: 792-796.
[12] van den Bergh F, Engelbrecht A P. A cooperative approach to particle swarm optimization [J]. IEEE Transactions on Evolutionary Computation, 2004, 8(3): 225-239.

Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization

Optimization for PID Controller of Cryogenic Ground Support Equipment Based on Cooperative Random Learning Particle Swarm Optimization

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