Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution

Miguel G. Villarreal-Cervantes, Alejandro Rodriguez-Molina, Consuelo Varinia Garcia-Mendoza, Ollin Penaloza-Mejia, Gabriel Sepulveda-Cervantes

Research output: Contribution to journalArticle

10 Citations (Scopus)

Abstract

© 2013 IEEE. The dc motor is one of the most fundamental electromechanical devices of mechatronic systems, which plays an important role in maintaining the accuracy in the execution of tasks. One of the main is sues in the accuracy and robustness of dc motor control system is how to optimally tune its parameters. In this paper, a multi-objective online tuning optimization approach is proposed to adaptively tune up the velocity control parameters of the permanent magnet dc motor. This approach simultaneously considers the modeled error and the corresponding sensitivity to choose the best compromise solution in the Pareto dominance-based selection process of solutions to deal the changing optimum solutions in the dynamic environment of the tuning approach based on online optimization method and moreover, the modified differential evolution with induced initial population based on non-dominated solution through a memory is proposed to guide the search into the feasible region, and to promote the exploitation of solutions found in the previous time interval. Simulation results verify that proposed modifications provide higher robustness and better quality in the velocity regulation control of the dc motor under parametric uncertainties, and also under discontinuous dynamic load, than multi-objective differential evolution, particle swarm optimization, and non-dominated sorting genetic algorithm-II.
Original languageAmerican English
Pages (from-to)20393-20407
Number of pages15
JournalIEEE Access
DOIs
StatePublished - 28 Sep 2017

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DC motors
Tuning
Controllers
Electromechanical devices
Velocity control
Mechatronics
Dynamic loads
Robustness (control systems)
Sorting
Particle swarm optimization (PSO)
Permanent magnets
Genetic algorithms
Control systems
Data storage equipment

Cite this

Villarreal-Cervantes, Miguel G. ; Rodriguez-Molina, Alejandro ; Garcia-Mendoza, Consuelo Varinia ; Penaloza-Mejia, Ollin ; Sepulveda-Cervantes, Gabriel. / Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution. In: IEEE Access. 2017 ; pp. 20393-20407.
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abstract = "{\circledC} 2013 IEEE. The dc motor is one of the most fundamental electromechanical devices of mechatronic systems, which plays an important role in maintaining the accuracy in the execution of tasks. One of the main is sues in the accuracy and robustness of dc motor control system is how to optimally tune its parameters. In this paper, a multi-objective online tuning optimization approach is proposed to adaptively tune up the velocity control parameters of the permanent magnet dc motor. This approach simultaneously considers the modeled error and the corresponding sensitivity to choose the best compromise solution in the Pareto dominance-based selection process of solutions to deal the changing optimum solutions in the dynamic environment of the tuning approach based on online optimization method and moreover, the modified differential evolution with induced initial population based on non-dominated solution through a memory is proposed to guide the search into the feasible region, and to promote the exploitation of solutions found in the previous time interval. Simulation results verify that proposed modifications provide higher robustness and better quality in the velocity regulation control of the dc motor under parametric uncertainties, and also under discontinuous dynamic load, than multi-objective differential evolution, particle swarm optimization, and non-dominated sorting genetic algorithm-II.",
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Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution. / Villarreal-Cervantes, Miguel G.; Rodriguez-Molina, Alejandro; Garcia-Mendoza, Consuelo Varinia; Penaloza-Mejia, Ollin; Sepulveda-Cervantes, Gabriel.

In: IEEE Access, 28.09.2017, p. 20393-20407.

Research output: Contribution to journalArticle

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AB - © 2013 IEEE. The dc motor is one of the most fundamental electromechanical devices of mechatronic systems, which plays an important role in maintaining the accuracy in the execution of tasks. One of the main is sues in the accuracy and robustness of dc motor control system is how to optimally tune its parameters. In this paper, a multi-objective online tuning optimization approach is proposed to adaptively tune up the velocity control parameters of the permanent magnet dc motor. This approach simultaneously considers the modeled error and the corresponding sensitivity to choose the best compromise solution in the Pareto dominance-based selection process of solutions to deal the changing optimum solutions in the dynamic environment of the tuning approach based on online optimization method and moreover, the modified differential evolution with induced initial population based on non-dominated solution through a memory is proposed to guide the search into the feasible region, and to promote the exploitation of solutions found in the previous time interval. Simulation results verify that proposed modifications provide higher robustness and better quality in the velocity regulation control of the dc motor under parametric uncertainties, and also under discontinuous dynamic load, than multi-objective differential evolution, particle swarm optimization, and non-dominated sorting genetic algorithm-II.

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