Genetic Algorithm-Based Parametrization of a PI Controller for DC Motor Control

Marko Mavrinac,Zlatan Car,Mario Šercer,Ivan Lorencin

doi:10.31803//tg-20201119185015

Abstract

This paper analyses the application of a genetic algorithm (GA) for the purpose of designing the control system with separately excited DC motor controlled according to the rotor angle. The presented research is based on the utilization of a mathematical model designed with separate electrical and mechanical sub-systems. Such an approach allows fine-tuning of PID controllers by using an evolutionary procedure, mainly GA. For purpose of PID tuning, the new fitness function which combines several step response parameters with the aim of forming a unique surface which is then minimized with a genetic algorithm. From the results, it can be seen that the elitism-based algorithm achieved better results compared to the eligibility-based selection. Such an algorithm achieved a fitness value of 0.999982 resulting in a steady-state error of 0.000584 rad. The obtained results indicate the possibility of applying a GA in the parameterization of the PID controller for DC motor control.

Highlights

Automatic control represents one of the key elements of modern society
The aim of this paper is to present a genetic algorithm (GA)-based method for PID controller parametrization that will be used for parametrization of 3 PI controllers used in the control of separately excited DC motor according to the rotor angle
From Tab. 3 it can be seen that there are no significant differences between GAs

Summary

Introduction

Feedback regulation represents a standard procedure in automatic control [1, 2]. Such a configuration is characterized by controller utilization [3]. PID controller parametrization, alongside mathematical modelling, represents one of the key challenges in the design of an automatic control system [5]. The classical approach used for PID parametrization and tuning is based on experimental design and mathematical modelling [6]. Such an approach requires expertise in the field of automatic control and electric drives [7]. Such an approach can be time-consuming and error-prone [8]

Objectives

Methods

Results

Conclusion