Fractional PID controllers tuned by evolutionary algorithms for robot trajectory control

Authors : Zafer BİNGÜL, Oğuzhan KARAHAN

Pages : 1123-1136

View : 29 | Download : 12

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :The aim of this paper is to compare the performances of a fractional order proportional integral derivative insert ignore into journalissuearticles values(FOPID); controller tuned with evolutionary algorithms for robot trajectory control. In order to make this comparison, a 2-degrees-of-freedom planar robot was controlled by a FOPID controller tuned with particle swarm optimization insert ignore into journalissuearticles values(PSO); and a real coded genetic algorithm insert ignore into journalissuearticles values(GA);. In order to see the effects of the cost functions on the optimum parameters of the FOPID controller, 3 different cost functions were used: the root mean squared error insert ignore into journalissuearticles values(MRSE);, mean absolute error insert ignore into journalissuearticles values(MAE);, and mean minimum fuel and absolute error insert ignore into journalissuearticles values(MMFAE);. In order to compare the performance of PSO and the GA under different conditions and to test the robustness of the FOPID controller tuned with these algorithms, the parameters of the system model and the given trajectory were changed and white noise was added to the system. All of the simulation results for the robot trajectory experiment show that the FOPID controller tuned by PSO has better performance than the FOPID controller tuned by the GA. Furthermore, the results obtained for the FOPID tuned by both PSO and the GA indicate the superiority of the proposed tuning approach for robot trajectory control.
Keywords : Fractional order controller, PSO, GA, robot trajectory control