Fractional Order PID Control using Ant Colony Optimization

2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES) Pub Date : 2016-07-04 DOI:10.1109/ICPEICES.2016.7853387

Richa Singh, Ambreesh Kumar, Rajneesh Sharma

{"title":"Fractional Order PID Control using Ant Colony Optimization","authors":"Richa Singh, Ambreesh Kumar, Rajneesh Sharma","doi":"10.1109/ICPEICES.2016.7853387","DOIUrl":null,"url":null,"abstract":"An Ant Colony Optimization (ACO) based Fractional Fuzzy PID controller is proposed in this paper. The resulting controller Ant Colony Fractional Fuzzy PID (AFrFPID) Controller incorporates the characteristics of the Ant Colony System and Fuzzy Control for controlling integer and fractional order plants. Fractional Order PID (FOPID) controllers show better performance for systems that have non-linear and time varying variables. However; the complexity of designing FOPID parameters is increased due to increase in tuning parameters (from 3 to 5). To obtain an initial estimate of these five parameters; the bio-inspired ACO algorithm is used. Ant Colony Optimization is a population based meta-heuristic technique which from the behavior of real ant colonies to find solutions to discrete optimization problems. Fuzzy Control is used to further fine tune the parameters for better control. MATLAB Simulations are presented and the performance of the AFrFPID controller is validated.","PeriodicalId":305942,"journal":{"name":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","volume":"56 2","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"21","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPEICES.2016.7853387","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 21

Abstract

An Ant Colony Optimization (ACO) based Fractional Fuzzy PID controller is proposed in this paper. The resulting controller Ant Colony Fractional Fuzzy PID (AFrFPID) Controller incorporates the characteristics of the Ant Colony System and Fuzzy Control for controlling integer and fractional order plants. Fractional Order PID (FOPID) controllers show better performance for systems that have non-linear and time varying variables. However; the complexity of designing FOPID parameters is increased due to increase in tuning parameters (from 3 to 5). To obtain an initial estimate of these five parameters; the bio-inspired ACO algorithm is used. Ant Colony Optimization is a population based meta-heuristic technique which from the behavior of real ant colonies to find solutions to discrete optimization problems. Fuzzy Control is used to further fine tune the parameters for better control. MATLAB Simulations are presented and the performance of the AFrFPID controller is validated.

查看原文本刊更多论文

基于蚁群优化的分数阶PID控制

提出了一种基于蚁群优化的分数阶模糊PID控制器。所得到的蚁群分数阶模糊PID (AFrFPID)控制器结合了蚁群系统和模糊控制的特点，用于控制整数阶和分数阶植物。分数阶PID (FOPID)控制器在具有非线性和时变变量的系统中表现出较好的控制性能。然而;由于调谐参数的增加(从3个增加到5个)，设计FOPID参数的复杂性增加。采用仿生蚁群算法。蚁群优化是一种基于群体的元启发式算法，从真实蚁群的行为出发，寻找离散优化问题的解。采用模糊控制对参数进行进一步微调，达到更好的控制效果。通过MATLAB仿真，验证了该AFrFPID控制器的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2016 IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES)

自引率

0.00%

发文量