Abstract
In this paper three intelligent evolutionary optimization approaches to design PID controller for a Gryphon Robot are presented and compared to t he results of a neuro-fuzzy system applied. The three applied approaches are artificial bee colony, shuffled frog leaping algorithm and particle swarm optimization. The design goal is to minimize the integral absolute error and reduce transient response by minimizing overshoot, settling time and rise time of step response. An Objective function of these indexes is defined and minimized applying the four optimization methods mentioned above. After optimization of the objective function, the optimal parameter s for the PID controller are adjusted. Simulation results show that FNN has a remarkable effect on decreasing the amount of settling time and rise -time and eliminating of steady-state error while the SFL algorithm performs better on steady -state error and the ABC algorithm is better on decreasing of overshoot. On the other hand PSO sounds to perform well on steady -state error only. In steady state manner all of the methods react robustly to the disturbance, but FNN shows more stability in transient response.
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