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

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Abstract

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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Acknowledgements

The authors acknowledge the support of the Secretaría de Investigación y Posgrado (SIP) under the Projects 20170783 and 20161030, and of the Consejo Nacional de Ciencia y Tecnología (CONACyT) under the Project 281728.

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Authors and Affiliations

  1. Posgraduate Department, Instituto Politécnico Nacional, CIDETEC, 07700, Mexico City, Mexico

    Alejandro Rodríguez-Molina, Miguel Gabriel Villarreal-Cervantes & Mario Aldape-Pérez

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  1. Alejandro Rodríguez-Molina
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  2. Miguel Gabriel Villarreal-Cervantes
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  3. Mario Aldape-Pérez
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Correspondence to Miguel Gabriel Villarreal-Cervantes.

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This article does not contain any studies with human participants or animals performed by any of the authors.

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Communicated by V. Loia.

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Rodríguez-Molina, A., Villarreal-Cervantes, M.G. & Aldape-Pérez, M. An adaptive control study for the DC motor using meta-heuristic algorithms. Soft Comput 23, 889–906 (2019). https://doi.org/10.1007/s00500-017-2797-y

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