Bio-inspired adaptive control strategy for the highly efficient speed regulation of the DC motor under parametric uncertainty

Alejandro Rodríguez-Molina, Miguel G. Villarreal-Cervantes, Jaime Álvarez-Gallegos, Mario Aldape-Pérez

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

© 2018 Elsevier B.V. The presence of parametric uncertainties decreases the performance in controlling dynamic systems such as the DC motor. In this work, an adaptive control strategy is proposed to deal with parametric uncertainties in the speed regulation task of the DC motor. This adaptive strategy is based on a bio-inspired optimization approach, where an optimization problem is stated and solved online by using a modification of the differential evolution optimizer. This modification includes a mechanism that promotes the exploration in the early generations and takes advantage of the exploitation power of the DE/best class in the last generations of the algorithm to find suitable optimal control parameters to control the DC motor speed efficiently. Comparative statistical analysis with other bio-inspired adaptive strategies and with linear, adaptive and robust controllers shows the effectiveness of the proposed bio-inspired adaptive control approach both in simulation and experimentation.
Original languageAmerican English
Pages (from-to)29-45
Number of pages24
JournalApplied Soft Computing Journal
DOIs
StatePublished - 1 Feb 2019

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DC motors
Uncertainty
Statistical methods
Dynamical systems
Controllers

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title = "Bio-inspired adaptive control strategy for the highly efficient speed regulation of the DC motor under parametric uncertainty",
abstract = "{\circledC} 2018 Elsevier B.V. The presence of parametric uncertainties decreases the performance in controlling dynamic systems such as the DC motor. In this work, an adaptive control strategy is proposed to deal with parametric uncertainties in the speed regulation task of the DC motor. This adaptive strategy is based on a bio-inspired optimization approach, where an optimization problem is stated and solved online by using a modification of the differential evolution optimizer. This modification includes a mechanism that promotes the exploration in the early generations and takes advantage of the exploitation power of the DE/best class in the last generations of the algorithm to find suitable optimal control parameters to control the DC motor speed efficiently. Comparative statistical analysis with other bio-inspired adaptive strategies and with linear, adaptive and robust controllers shows the effectiveness of the proposed bio-inspired adaptive control approach both in simulation and experimentation.",
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Bio-inspired adaptive control strategy for the highly efficient speed regulation of the DC motor under parametric uncertainty. / Rodríguez-Molina, Alejandro; Villarreal-Cervantes, Miguel G.; Álvarez-Gallegos, Jaime; Aldape-Pérez, Mario.

In: Applied Soft Computing Journal, 01.02.2019, p. 29-45.

Research output: Contribution to journalArticleResearchpeer-review

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AU - Aldape-Pérez, Mario

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AB - © 2018 Elsevier B.V. The presence of parametric uncertainties decreases the performance in controlling dynamic systems such as the DC motor. In this work, an adaptive control strategy is proposed to deal with parametric uncertainties in the speed regulation task of the DC motor. This adaptive strategy is based on a bio-inspired optimization approach, where an optimization problem is stated and solved online by using a modification of the differential evolution optimizer. This modification includes a mechanism that promotes the exploration in the early generations and takes advantage of the exploitation power of the DE/best class in the last generations of the algorithm to find suitable optimal control parameters to control the DC motor speed efficiently. Comparative statistical analysis with other bio-inspired adaptive strategies and with linear, adaptive and robust controllers shows the effectiveness of the proposed bio-inspired adaptive control approach both in simulation and experimentation.

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