Optimal Adjustment of Evolutionary Algorithm-Based Fuzzy Controller for Driving Electric Motor with Computer Interface

This study focused on the development of a fuzzy system based on evolutionary algorithms (EA) to obtain the optimum parameters of the fuzzy controller and increase the convergence speed and accuracy of the controller. The aim of the study is to design a fuzzy controller without expert knowledge by using evolutionary genetic algorithms, and apply it in a DC motor. The design is based on the optimization of rule bases of the fuzzy controller. In the learning stage, the obtained rule base fitness values are measured by working the rule base on the controller. The learning stage is repeated with the termination criteria. The proposed fuzzy controller is applied on the DC motor from a PC program using an interface circuit. Simulated and experimental results have shown that the designed fuzzy controller provides system responses with high performance, low steady-state error for DC motor control, and low settling time.

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