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

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


© 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
StatePublished - 13 Feb 2019


Dive into the research topics of 'An adaptive control study for the DC motor using meta-heuristic algorithms'. Together they form a unique fingerprint.

Cite this