PID控制器
控制理论(社会学)
直流电动机
计算机科学
模糊逻辑
推论
变量(数学)
模糊控制系统
自适应神经模糊推理系统
控制器(灌溉)
遗传算法
控制工程
人工智能
数学
控制(管理)
机器学习
工程类
温度控制
数学分析
农学
电气工程
生物
作者
Kewei Song,Ze Zhang,Wang Hu,Hui Fang
标识
DOI:10.1177/09544062211060303
摘要
In this study, we propose a novel robust online self-adaptive Proportional-Integral-Derivative (PID) control design for Brushless DC Motor (BLDCM) speed system under different operating conditions. The online adaptive tuning for PID parameters is realized accurately by optimizing the control rules of variable universe fuzzy inference with a modified genetic algorithm (GA). Based on the variable fuzzy inference theory, the method of solving contraction–expansion factor in real-time through fuzzy inference is proposed. Furthermore, the process to optimize two inference rules by GA is improved to get optimal control rules for adjusting PID parameters. Finally, multiple sets of simulations and experiments are conducted to validate the proposed controller in different conditions by building Simulink models and setting up experiment platforms. The results of this study not only demonstrate the effectiveness of the proposed controller but also provide technical suggestions for the speed control of BLDCM.
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