PID Parameter Optimization Based on Improved Particle Swarm Optimization Algorithm
Received date: 2016-01-31
Revised date: 2016-03-03
Online published: 2017-09-30
PID parameter optimization is a hot topic in control engineering. An improved particle swarm optimization (PSO) algorithm is proposed to optimize PID parameters. PID parameters are selected and the system performance is improved. The factors of evolution speed and aggregation degree of the swarm are introduced to the algorithm to improve the weight to improve the velocity update formula. A flying time factor is then introduced to improve the location update formula. Advantage of the algorithm is shown by three typical functions, indicating improvement of convergence speed and search efciency. A typical second order controlled model is selected as an object for research, and results of the algorithm are compared with other PSO algorithms. Experiments show that the optimized PID parameters obtained by using the improved PSO algorithm can achieve good control performance.
JIANG Chang-hong, ZHANG Yong-heng, WANG Sheng-hui . PID Parameter Optimization Based on Improved Particle Swarm Optimization Algorithm[J]. Journal of Applied Sciences, 2017 , 35(5) : 667 -674 . DOI: 10.3969/j.issn.0255-8297.2017.05.012
[1] 杨智,陈志堂,范正平,李晓东. 基于改进粒子群优化算法的PID控制器整定[J]. 控制理论与应用,2010, 27(10):1345-1352. Yang Z, Chen Z T, Fan Z P, Li X D. Tuning of PID controller based on improved particleswarm-optimization[J]. Control Theory & Application, 2010, 27(10):1345-1352. (in Chinese)
[2] 余胜威,曹中清. 基于人群搜索算法的PID控制器参数优化[J]. 计算机仿真,2014, 31(9):347-350. Yu S W, Cao Z Q. Optimization parameters of PID controller parameters based on seeker optimization algorithm[J]. Computer Simulation, 2014, 31(9):347-350. (in Chinese)
[3] Echevarría L C, Santiago O L, Fajardo J A H, Neto A J S, Sánchez D J. A variant of the particle swarm optimization for the improvement of fault diagnosis in industrial systems via faults estimation[J]. Engineering Applications of Artifcial Intelligence, 2014, 28:36-51.
[4] Kennedy J, Eberhart R. Particle swarm optimization[C]//IEEE International Conference on Neural Networks, Perth, Australia, 1995:1942-1948.
[5] 孟丽,韩璞,任燕燕,王东风. 基于多目标粒子群的PID控制器设计[J]. 计算机仿真,2013, 30(7):388-391. Meng L, Han P, Ren Y Y, Wang D F. Design of PID Controller based on multi-objective particle swarm optimization algorithm[J]. Computer Simulation, 2013, 30(7):388-391. (in Chinese)
[6] 陶新民,刘福荣,刘玉,童智靖. 一种多尺度协同变异的粒子群优化算法[J]. 软件学报,2012, 23(7):1805-1815. Tao X M, Liu F R, Liu Y, Tong Z J. Multi-scale cooperative mutation particle swarm optimization algorithm[J]. Journal of Software, 2012, 23(7):1805-1815. (in Chinese)
[7] Ni Q J, Zhang Z Z, Wang Z Z, Xing H C. Dynamic probabilistic particle swarm optimization based on varying multi-cluster structure[J]. Journal of Software, 2009, 20(2):339-349.
[8] 王建林,吴佳欢,张超然,赵利强,于涛. 基于自适应进化学习的多目标粒子群优化算法[J]. 控制与决策,2014, 29(2):1-6. Wang J L, Wu J H, Zhang C R, Zhao L Q, Yu T. Constrained multi-objective particle swarm optimization algorithm based on self-adaptive evolutionary learning[J]. Control and Decision, 2014, 29(2):1-6. (in Chinese)
[9] Kennedy J. The particle swarm:social adaptation of knowledge[C]//IEEE International Conference on Evolutionary Computation, 1997:303-308.
[10] 黄泽霞,俞攸红,黄德才. 惯性权自适应调整的量子粒子群优化算法[J]. 上海交通大学学报,2012, 46(2):228-232. Huang Z X, Yu Y H, Huang D C. Quantum-behaved particle swarm algorithm with selfadapting adjustment of inertia weight[J]. Journal of Shanghai Jiaotong University, 2012, 46(2):228-232. (in Chinese)
[11] Clerc M, Kennedy J. The particle swarm-explosion, stability and convergence in a multidimensional complex space[J]. IEEE Trans. Evol. Comput. 2002, 6:58-73.
[12] Kennedy J, Eberhart R. Particle swarm optimization[C]//IEEE International Conference on Neural Networks, Perth, Australia, 1995:1942-1948.
[13] Poli R. An analysis of publications on particle swarm optimization applications[D]. Department of Computer Science, University of Essex, 2007.
[14] 米根锁,梁利,杨润霞. 灰色变异粒子群算法载客车流量预测中的应用[J]. 计算机工程与科学,2015, 31(1):361-363. Mi G S, Liang L, Yang R X. Application of the grey mutation particle swarm algorithm in urban public transport passenger volume prediction[J]. Computer Engineering and Science, 2015, 31(1):361-363. (in Chinese)
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