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.

编辑推荐

Metrics

阅读次数

全文

438

HTML			PDF

最新录用	在线预览	正式出版	最新录用	在线预览	正式出版
0	0	0	0	0	438

来源	本网站	其他网站

次数	5	433
比例	1%	99%

摘要

945

最新录用	在线预览	正式出版

0	0	945

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

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 11

编辑推荐

Metrics

本文评价

[1]	PENG Pai, CHEN Cong , YANG Yongsheng . Particle Swarm Optimization Based on Hybrid Kalman Filter and Particle Filter [J]. J Shanghai Jiaotong Univ Sci, 2020, 25(6): 681-688.
[2]	QIN Zhichang, XIN Ying, SUN Jianqiao . Multi-Objective Optimal Feedback Controls for Under-Actuated Dynamical System[J]. Journal of Shanghai Jiao Tong University(Science), 2020, 25(5): 545-552.
[3]	LI Jie (李杰), LIU Yongzhi (刘勇智), SHAN Chenglong (鄯成龙), DAI Cong (戴聪). Implementation of Simplified Fractional-Order PID Controller Based on Modified Oustaloup's Recursive Filter[J]. Journal of Shanghai Jiao Tong University (Science), 2020, 25(1): 44-50.
[4]	LIU Kai (刘凯), WU Yang (吴阳), GE Zhishang (葛志尚), WANG Yangwei (王扬威), XU Jiaqi (许嘉. Adaptive Multi-Objective Optimization of Bionic Shoulder Joint Based on Particle Swarm Optimization[J]. Journal of Shanghai Jiao Tong University (Science), 2018, 23(4): 550-.
[5]	JIAO Qinglong (焦庆龙), XU Da (徐达). A Discrete Bat Algorithm for Disassembly Sequence Planning[J]. sa, 2018, 23(2): 276-285.
[6]	BIAN Li1* (边莉), BIAN Chen-yuan1 (边晨源), WANG Shu-min2 (王书民). Large Thinned Array Design Based on Multi-objective Cross Entropy Algorithm[J]. 上海交通大学学报（英文版）, 2015, 20(4): 437-442.
[7]	MAO Li1 (毛力), SONG Yi-chun1* (宋益春), LI Yin1 (李引),YANG Hong2 (杨弘), XIAO Wei2 (肖炜). Research of Improved Fuzzy c-means Algorithm Based on a New Metric Norm[J]. 上海交通大学学报（英文版）, 2015, 20(1): 51-55.
[8]	YU Hai-yang1* (于海洋), ZHANG Shi-lian1 (张世联), LI Cong2 (李聪), WU Shao-bo1 (武少波). Particle Swarm Approach for Structural Optimization of Battleship Strength Deck Under Air Blast[J]. 上海交通大学学报（英文版）, 2014, 19(4): 481-487.
[9]	WANG Ying-lin1,2 (王英林), XU He-ming2* (徐鹤鸣). Multiobjective Particle Swarm Optimization Without the Personal Best[J]. 上海交通大学学报（英文版）, 2014, 19(2): 155-159.
[10]	QU Guo-dong* (屈国栋), LOU Zhang-hua (楼章华). Application of Particle Swarm Algorithm in the Optimal Allocation of Regional Water Resources Based on Immune Evolutionary Algorithm[J]. 上海交通大学学报（英文版）, 2013, 18(5): 634-640.
[11]	HAN Yi (韩毅), CAI Jian-hu (蔡建湖), IKOU Kaku, LI Yan-lai (李延来) CHE. Evolutionary Algorithms for Solving Unconstrained Multilevel Lot-Sizing Problem with Series Structure[J]. 上海交通大学学报（英文版）, 2012, 17(1): 39-044.