An MEA-Tuning Method for Design of the PID Controller

Abstract

The optimization and tuning of parameters is very important for the performance of the PID controller. In this paper, a novel parameter tuning method based on the mind evolutionary algorithm (MEA) was presented. The MEA firstly transformed the problem solutions into the population individuals embodied by code and then divided the population into superior subpopulations and temporary subpopulations and used the similar taxis and dissimilation operations for searching the global optimal solution. In order to verify the control performance of the MEA, three classical functions and five typical industrial process control models were adopted for testing experiments. Experimental results indicated that the proposed approach was feasible and valid: the MEA with the superior design feature and parallel structure could memorize more evolutionary information, generate superior genes, and enhance the efficiency and effectiveness for searching global optimal parameters. In addition, the MEA-tuning method can be easily applied to real industrial practices and provides a novel and convenient solution for the optimization and tuning of the PID controller.

NSGA-II-Based Parameter Tuning Method and GM(1,1)-Based Development of Fuzzy Immune PID Controller for Automatic Train Operation System

“…(1) In order to assist the tuning of the FI-PID controller, a framework based on NSGA-II was proposed. e optimization of parameters is very important for the performance of the PID controller or the FI-PID controller [32,36]. e problem is often multiobjective, and NSGA-II can solve almost any multiobjective optimization problem [7].…”

Fırçasız Doğru Akım Motorlarının Hız Kontrolünü Gerçekleştirmek İçin PID/PD Kontrolcü Tasarımı ve Performans İncelemesi

“…Ancak yapılan çalışmalara bakıldığında geleneksel parametre ayarlama yöntemleri; yüksek aşma miktarı, düşük doğruluk oranı ve ayar süresinin uzun olması gibi dezavantajlarından dolayı yapay zeka optimizasyon algoritmaları kadar başarılı olamamaktadır (Y. Zhang, Zhang, & Dong, 2019). Çalışmada yapay zeka optimizasyon algoritmalarından olan BOA yöntemi kontrolcü parametrelerinin belirlenmesi için kullanılmıştır.…”

Energy optimization of an electric car using losses minimization and intelligent predictive torque control

“…18 Their applications to the problems of control systems have proven to be very successful. 19,20 In this work, an intelligent technique based on the PSO for adjusting these sensitive parameters is proposed. 21,22 This technique is based on the movement imitation of swarms in the search for their food by choosing an optimal trajectory.…”