论文信息 - An adaptive control study for a DC motor using meta-heuristic algorithms

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

Abstract In this work, a comparative study of the use of different meta-heuristic techniques in the adaptive control for the speed regulation of the DC motor with parameters uncertainties is presented. Several adaptive controllers based on the optimizers of Differential Evolution (DE), Particle Swarm Optimization (PSO), Bat Algorithm (BAT), Firefly Algorithm (FFA) and Wolf Search Algorithm (WSA) are proposed in order to on-line tune the parameters of the DC motor. These parameters are used in calculating the control signal. Simulations show the efficacy of each control strategy. Given the results, the controller based on PSO is one of the most promising alternatives for this approach.

Miguel G. Villarreal-Cervantes | Alejandro Rodríguez-Molina | Mario Aldape-Pérez

[1] K. Deb. An Efficient Constraint Handling Method for Genetic Algorithms , 2000 .

[2] Riccardo Poli,et al. Particle swarm optimization , 1995, Swarm Intelligence.

[3] Kyoung Kwan Ahn,et al. Online tuning fuzzy PID controller using robust extended Kalman filter , 2009 .

[4] Vineet Kumar,et al. An online tuned novel nonlinear PI controller for stiction compensation in pneumatic control valves. , 2015, ISA transactions.

[5] Kuo-Kai Shyu,et al. On-line gain-tuning IP controller using real-coded genetic algorithm , 2004 .

[6] Francisco Herrera,et al. A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[7] Simon Fong,et al. Wolf search algorithm with ephemeral memory , 2012, Seventh International Conference on Digital Information Management (ICDIM 2012).