收稿日期: 2016-01-31
修回日期: 2016-03-03
网络出版日期: 2017-09-30
基金资助
吉林省科技发展计划项目基金（No.20140204024GX）资助
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参数优化是控制领域的热点，其控制效果与比例、积分、微分参数有直接关系.为了改善系统性能，提出用一种改进的粒子群优化算法对PID控制器参数进行优化.该算法引入进化速度因子和聚集度因子对权值进行改进，进而改进了速度更新公式，并引入飞行时间因子以改进位置更新公式.通过3种典型函数证明了该算法的优越性，加快了收敛速度，提高了寻优效率.以典型二阶被控模型为研究对象，将上述算法与其他粒子群算法进行对比，表明改进的粒子群算法得到的PID参数具有更好的控制性能.
关键词： 权值; 改进的粒子群优化算法; PID参数优化; 飞行时间因子
姜长泓, 张永恒, 王盛慧 . 基于改进粒子群优化算法的PID控制器参数优化[J]. 应用科学学报, 2017 , 35(5) : 667 -674 . DOI: 10.3969/j.issn.0255-8297.2017.05.012
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.
[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)
/
| 〈 |
|
〉 |
