基于改进蝙蝠算法的模糊PID规则优化研究

doi:10.19678/j.issn.1000-3428.0055538

计算机工程 ›› 2020, Vol. 46 ›› Issue (8): 305-312. doi: 10.19678/j.issn.1000-3428.0055538

基于改进蝙蝠算法的模糊PID规则优化研究

杜学武^1,2, 张明新², 沙广涛^2,3, 伍秋玉³

1. 苏州大学计算机科学与技术学院, 江苏苏州 215006;
2. 常熟理工学院计算机科学与工程学院, 江苏常熟 215500;
3. 中国矿业大学计算机科学与技术学院, 江苏徐州 221116

收稿日期:2019-07-22 修回日期:2019-10-14 发布日期:2019-10-24
作者简介:杜学武(1994-),男,硕士研究生,主研方向为数据挖掘、智能控制;张明新,教授;沙广涛、伍秋玉,硕士研究生。
基金资助:
国家自然科学基金（61173130）。

Research of Fuzzy PID Rule Optimization Based on Improved Bat Algorithm

DU Xuewu^1,2, ZHANG Mingxin², SHA Guangtao^2,3, WU Qiuyu³

1. School of Computer Science and Technology, Soochow University, Suzhou, Jiangsu 215006, China;
2. School of Computer Science and Engineering, Changshu Institute of Technology, Changshu, Jiangsu 215500, China;
3. School of Computer Science and Technology, China University of Mining and Technology, Xuzhou, Jiangsu 221116, China

Received:2019-07-22 Revised:2019-10-14 Published:2019-10-24

摘要/Abstract

摘要： 模糊控制规则是模糊PID控制器的核心，在对模糊控制规则优化时通常采用特定的方式寻优弱化模糊控制规则间的关联性，存在影响计算效率以及控制精度等问题。为此，在蝙蝠算法（BA）寻优方式的基础上，提出一种改进的蝙蝠算法（IBA）对模糊控制规则进行优化。通过模糊控制规则间关联性设计邻域搜索算子以提高BA的搜索精度，再引入混沌变异算子避免BA陷入局部最优，以ITAE值作为性能指标对模糊PID控制系统进行评价。仿真结果表明，与粒子群算法、遗传算法和改进的蚁群算法的优化效果相比，IBA优化模糊控制规则后的模糊PID控制器的调节时间与超调量均较小，且提高了控制精度与计算效率。

关键词: 模糊PID控制, 模糊控制规则优化, 蝙蝠算法, 邻域搜索算子, 混沌变异算子

Abstract: Fuzzy control rules are the core of fuzzy PID controllers.Optimization of fuzzy control rules usually requires specific methods to optimize and weaken the correlation between fuzzy control rules,which affects the calculation efficiency and control accuracy.Therefore,this paper proposes an Improved Bat Algorithm(IBA) to optimize fuzzy control rules on the basis of the optimization method of the Bat Algorithm(BA).The neighborhood search operator is designed by the correlation between fuzzy control rules to improve the search accuracy of BA,and then the chaos mutation operator is introduced to avoid BA from falling into the local optimum.The fuzzy PID control system is evaluated with ITAE value as the performance index.Results of the simulation show that compared with the optimization performance of the particle swarm algorithm,genetic algorithm and improved ant colony algorithm,IBA reduces the adjustment time and overshoot of the fuzzy PID controller with optimized fuzzy control rules,and improves the control accuracy and calculation efficiency.

Key words: fuzzy PID control, fuzzy control rule optimization, Bat Algorithm(BA), neighborhood search operator, chaotic mutation operator

中图分类号:

TP273.4

杜学武, 张明新, 沙广涛, 伍秋玉. 基于改进蝙蝠算法的模糊PID规则优化研究[J]. 计算机工程, 2020, 46(8): 305-312.

DU Xuewu, ZHANG Mingxin, SHA Guangtao, WU Qiuyu. Research of Fuzzy PID Rule Optimization Based on Improved Bat Algorithm[J]. Computer Engineering, 2020, 46(8): 305-312.

https://www.ecice06.com/CN/Y2020/V46/I8/305

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