PID Controller Parameter Optimization  Based on Teaching-learning Optimization Algorithm

doi:10.3969/j.issn.1000-3428.2015.08.057

Computer Engineering

Previous Articles Next Articles

PID Controller Parameter Optimization Based on Teaching-learning Optimization Algorithm

HE Xueming ¹,MIAO Yannan ²,LUO Zailei ²

(1.Department of Mechanical and Electrical Management,China Maritime Police Academy,Ningbo 315801,China; 2.Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology,Wuhan 430074,China)

Received:2014-08-21 Online:2015-08-15 Published:2015-08-15

基于教与学优化算法的PID控制器参数寻优

何学明¹,苗燕楠²,罗再磊²

(1.公安海警学院机电管理系,浙江宁波 315801; 2.华中科技大学数字制造装备与技术国家重点实验室,武汉 430074)

作者简介:何学明(1966－),男,教授、博士,主研方向:机电智能控制,机械系统动力学及控制;苗燕楠,硕士研究生;罗再磊,博士研究生。
基金资助:
高等学校博士学科点专项科研基金资助项目(20110142130010);公安部重点研究计划基金资助项目(2011ZDYJHJXY012)。

Abstract

Abstract: The Proportional-Integral-Derivative(PID)controller parameters tuning,is seeking the optimal value in the space of three parameters to achieve the optimal control performance of the system.Teaching-learning optimization is a new kind of swarm intelligence optimization algorithm.The algorithm is simple and easy to understand,and has less parameters,high solving speed,high precision and strong convergence ability.A new PID controller parameters tuning method based on the teaching-learning optimization algorithm is proposed.The parameters optimization of PID controller is realized by using teaching-learning,and simulation examples are done by Matlab.In the simulation examples,compared with the PID controller parameters tuning methods based on Particle Swarm Optimization(PSO)algorithm and Genetic Algorithm(GA),the results show that this method is simple with high precision.And it can effectively achieve the self-tuning of PID controller parameters quickly.

Key words: teaching-learning optimization algorithm, Proportional-Integral-Derivative(PID) controller, parameter self-tuning, Genetic Algorithm(GA), Particle Swarm Optimization(PSO)algorithm

摘要： PID控制器的参数整定表现为在3个参数空间中寻求最优值,使得系统的控制性能达到最优。教与学优化算法是一种新兴的群智能优化算法。为加强PID控制器的参数整定,基于教与学优化算法,提出一种PID控制器参数自整定方法,实现PID控制器的参数寻优。利用Matlab进行实例仿真,结果表明,与基于粒子群算法和遗传算法的PID参数整定方法进行比较,该方法参数简单、精度高,可快速有效地实现PID控制器参数的自整定。

关键词: 教与学优化算法, PID控制器, 参数自整定, 遗传算法, 粒子群优化算法

CLC Number:

TP301.6

HE Xueming,MIAO Yannan,LUO Zailei. PID Controller Parameter Optimization Based on Teaching-learning Optimization Algorithm[J]. Computer Engineering, doi: 10.3969/j.issn.1000-3428.2015.08.057.

何学明,苗燕楠,罗再磊. 基于教与学优化算法的PID控制器参数寻优[J]. 计算机工程, doi: 10.3969/j.issn.1000-3428.2015.08.057.

/ / Recommend / Download Citations

URL: http://www.ecice06.com/EN/10.3969/j.issn.1000-3428.2015.08.057

http://www.ecice06.com/EN/Y2015/V41/I8/313

References

参考文献［1］Wang Xin,Li Ran,Wang Yanghua,et al.Self-tuning PID Controller with Variable Parameters Based on Particle Swarm Optimization［C］//Proceedings of the 3rd International Conference on Intelligent System Design and Engineering Applications.Berlin,Germany:Springer,2012:1264-1267. ［2］陶永华,尹怡欣,葛芦生.新型PID控制及其应用［M］.北京:机械工业出版社,1998. ［3］Rao R V,Savsani V,Vakharia D P.Teaching-learning-based Optimization:A Novel Method for Constrained Mechanical Design Optimization Problems［J］.Com-puter-aided Design,2011,43(3):303-315. ［4］湛锋,魏星,郭建全,等.基于改进粒子群优化算法的PID参数整定［J］.继电器,2005,33(19):23-27. ［5］拓守恒,雍龙泉,邓方安.“教与学”优化算法研究综述［J］.计算机应用研究,2013,30(7):1933-1938. ［6］Rao R V,Patel V.An Elitist Teaching-learning-based Optimization Algorithm for Solving Complex Con-strained Optimization Problems［J］.International Journal of Industrial Engineering Computations,2012,3(4):535-560. ［7］胡伟,徐福缘.基于改进粒子群算法的PID控制器参数自整定［J］.计算机应用研究,2012,29(5):1791-1794. ［8］史峰,王辉,郁磊,等.MATLAB智能算法30个案例分析［M］.北京:北京航空航天大学出版社,2011. ［9］Oi A,Nakazawa C,Matsui T,et al.PID Optimal Tuning Method by Particle Swarm Optimization［C］//Proceedings of SICE Annual Conference.Washington D.C.,USA:IEEE Press,2008:3470-3473. ［10］郭大庆,李晓,赵永进.基于改进PSO算法的PID参数自整定［J］.计算机工程,2007,33(18):202-204. ［11］刘金琨.先进PID控制MATLAB仿真［M］.3版.北京:电子工业出版社,2011. ［12］Das S,Konar A.Improving Particle Swarm Optimization with Differentially Perturbed Velocity［C］//Proceedings of Conference on Genetic and Evolutionary Computa-tion.New York,USA:ACM Press,2005:177-184. ［13］巩敦卫,张勇,张建化,等.新型粒子群优化算法［J］.控制理论与应用,2008,25(1):111-114. 编辑顾逸斐

[1]	BAI Zhixu, WANG Hengjun. Adversarial Example Generation Method Based on Improved Genetic Algorithm [J]. Computer Engineering, 2023, 49(5): 139-149.
[2]	SANG Yongxuan, WEI Jiangpo, WANG Bo, SONG Ying. Hybrid Heuristic Task Offloading Algorithm with Edge Caching Mechanism [J]. Computer Engineering, 2023, 49(4): 149-158.
[3]	WU Tiezhou, ZOU Zhi, JIANG Ben, ZHANG Xiaoxing. Short-Term Load Forecasting Based on TLBGA-GRU Neural Network [J]. Computer Engineering, 2022, 48(11): 69-76.
[4]	DU Xiuli, ZHOU Min, Lü Yana, QIU Shaoming. Effectiveness Evaluation of Equipment Support System Based on Optimized RBF Neural Network [J]. Computer Engineering, 2021, 47(9): 282-287,296.
[5]	WEI Xiuran, WANG Feng. Data Replication Strategy of Cloud Storage Based on Coordinator and Genetic Algorithm [J]. Computer Engineering, 2021, 47(8): 124-130,139.
[6]	ZHENG Juanyi, CUI Zhuo, SU Hailong, YIN Shuaishuai, LIU Yaoyao. Intelligent Method Based on Improved GA-Elman for Predicting the Wireless Propagation Loss [J]. Computer Engineering, 2021, 47(7): 155-160,167.
[7]	YANG Tian, YANG Jun. Offloading Decision and Resource Allocation Strategy in Mobile Edge Computing [J]. Computer Engineering, 2021, 47(2): 19-25.
[8]	LU Rongxiu, HE Quanheng, YANG Hui, ZHU Jianyong. Multi-Component Content Prediction of Rare Earth Mixed Solution Based on GA-ELM [J]. Computer Engineering, 2021, 47(1): 284-290,297.
[9]	WANG Yan, GE Haibo, FENG Anqi. Computation Offloading Strategy in Cloud-Assisted Mobile Edge Computing [J]. Computer Engineering, 2020, 46(8): 27-34.
[10]	LUO Binshen, LIU Limin, DONG Jian, LIU Jingqi. Radar New Jamming Identification Based on SAE-GA-SVM Model [J]. Computer Engineering, 2020, 46(6): 281-287.
[11]	ZHANG Chunfu, WANG Song, WU Yadong, WANG Yong, ZHANG Hongying. Diabetes Risk Prediction Based on GA_Xgboost Model [J]. Computer Engineering, 2020, 46(3): 315-320.
[12]	DING Feihong,LIU Peng,LU Tun,GU Ning,DING Xianghua,YANG Baoming,DAI Wenqi,ZOU Chaojun. Short-term prediction model of building energy consumption based on genetically optimized decision tree [J]. Computer Engineering, 2019, 45(6): 280-289,296.
[13]	LI Suikai,LI Yitao,SUN Weiwei,QIN Tao,HUANG Xiusong. A storage space allocation algorithm for export containers in automated container terminals [J]. Computer Engineering, 2019, 45(5): 272-278,284.
[14]	ZHANG Yupei,ZHAO Zhijin,ZHENG Shilian. Design Algorithm of Target Channel Sequence for Spectrum Handoff Based on Multi-objective Genetic [J]. Computer Engineering, 2019, 45(2): 122-128.
[15]	SUN Shiqing, PENG Jianhua, YOU Wei, LI Yingle. Service Function Chain Deployment Method Based on Online Instance Configuration [J]. Computer Engineering, 2019, 45(12): 71-78.

Please choose a citation manager

Content to export

PID Controller Parameter Optimization Based on Teaching-learning Optimization Algorithm

基于教与学优化算法的PID控制器参数寻优

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments

模态框（Modal）标题

Please choose a citation manager

Content to export

PID Controller Parameter Optimization Based on Teaching-learning Optimization Algorithm

基于教与学优化算法的PID控制器参数寻优

PDF

Knowledge

Cited

Abstract

Cite this article

share this article

References

Related Articles 15

Recommended Articles

Metrics

Comments