An adaptive control study for the DC motor using meta-heuristic algorithms

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

© 2017, Springer-Verlag GmbH Germany, part of Springer Nature. In this work, a comparative study of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. The adaptive control is established as the online solution of a constrained dynamic optimization problem. Several adaptive strategies based on Differential Evolution, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, Wolf Search Algorithm and Genetic Algorithm are proposed in order to online tune the parameters of the DC motor control. Simulation results show that proposed adaptive control strategies are a viable alternative to regulate the speed of the motor subject to different operation scenarios. The statistical analysis given in this work shows the features and the differences among strategies, their feasibility to set them up experimentally and also a new hybrid strategy to efficiently solve the problem. In addition, comparative analysis with a robust control approach reveal the advantages of the adaptive strategy based on meta-heuristic techniques in the velocity regulation of the DC motor.
Original languageAmerican English
Pages (from-to)889-906
Number of pages798
JournalSoft Computing
DOIs
StatePublished - 13 Feb 2019

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Adaptive Strategies
DC Motor
DC motors
Heuristic algorithms
Metaheuristics
Heuristic algorithm
Adaptive Control
Dynamic Optimization Problems
Motor Control
Parameter Uncertainty
Constrained Optimization Problem
Differential Evolution
Robust Control
Particle Swarm Optimization Algorithm
Comparative Analysis
Search Algorithm
Comparative Study
Statistical Analysis
Control Strategy
Robust control

Cite this

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title = "An adaptive control study for the DC motor using meta-heuristic algorithms",
abstract = "{\circledC} 2017, Springer-Verlag GmbH Germany, part of Springer Nature. In this work, a comparative study of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. The adaptive control is established as the online solution of a constrained dynamic optimization problem. Several adaptive strategies based on Differential Evolution, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, Wolf Search Algorithm and Genetic Algorithm are proposed in order to online tune the parameters of the DC motor control. Simulation results show that proposed adaptive control strategies are a viable alternative to regulate the speed of the motor subject to different operation scenarios. The statistical analysis given in this work shows the features and the differences among strategies, their feasibility to set them up experimentally and also a new hybrid strategy to efficiently solve the problem. In addition, comparative analysis with a robust control approach reveal the advantages of the adaptive strategy based on meta-heuristic techniques in the velocity regulation of the DC motor.",
author = "Alejandro Rodr{\'i}guez-Molina and Villarreal-Cervantes, {Miguel Gabriel} and Mario Aldape-P{\'e}rez",
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An adaptive control study for the DC motor using meta-heuristic algorithms. / Rodríguez-Molina, Alejandro; Villarreal-Cervantes, Miguel Gabriel; Aldape-Pérez, Mario.

In: Soft Computing, 13.02.2019, p. 889-906.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Rodríguez-Molina, Alejandro

AU - Villarreal-Cervantes, Miguel Gabriel

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N2 - © 2017, Springer-Verlag GmbH Germany, part of Springer Nature. In this work, a comparative study of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. The adaptive control is established as the online solution of a constrained dynamic optimization problem. Several adaptive strategies based on Differential Evolution, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, Wolf Search Algorithm and Genetic Algorithm are proposed in order to online tune the parameters of the DC motor control. Simulation results show that proposed adaptive control strategies are a viable alternative to regulate the speed of the motor subject to different operation scenarios. The statistical analysis given in this work shows the features and the differences among strategies, their feasibility to set them up experimentally and also a new hybrid strategy to efficiently solve the problem. In addition, comparative analysis with a robust control approach reveal the advantages of the adaptive strategy based on meta-heuristic techniques in the velocity regulation of the DC motor.

AB - © 2017, Springer-Verlag GmbH Germany, part of Springer Nature. In this work, a comparative study of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. The adaptive control is established as the online solution of a constrained dynamic optimization problem. Several adaptive strategies based on Differential Evolution, Particle Swarm Optimization, Bat Algorithm, Firefly Algorithm, Wolf Search Algorithm and Genetic Algorithm are proposed in order to online tune the parameters of the DC motor control. Simulation results show that proposed adaptive control strategies are a viable alternative to regulate the speed of the motor subject to different operation scenarios. The statistical analysis given in this work shows the features and the differences among strategies, their feasibility to set them up experimentally and also a new hybrid strategy to efficiently solve the problem. In addition, comparative analysis with a robust control approach reveal the advantages of the adaptive strategy based on meta-heuristic techniques in the velocity regulation of the DC motor.

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