TY - JOUR
T1 - Multi-Objective On-Line Optimization Approach for the DC Motor Controller Tuning Using Differential Evolution
AU - Villarreal-Cervantes, Miguel G.
AU - Rodriguez-Molina, Alejandro
AU - Garcia-Mendoza, Consuelo Varinia
AU - Penaloza-Mejia, Ollin
AU - Sepulveda-Cervantes, Gabriel
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2017/9/28
Y1 - 2017/9/28
N2 - 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 issues 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.
AB - 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 issues 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.
KW - Controller tuning
KW - DC motor
KW - intelligent control
KW - multi-objective evolutionary optimization
KW - on-line tuning optimization method
UR - http://www.scopus.com/inward/record.url?scp=85030789262&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2017.2757959
DO - 10.1109/ACCESS.2017.2757959
M3 - Artículo
SN - 2169-3536
VL - 5
SP - 20393
EP - 20407
JO - IEEE Access
JF - IEEE Access
M1 - 8053757
ER -