DC Motor neuro-fuzzy controller using PSO identification

DC Motor controller is the most important issue in many applications. There are trade-off between the performance and the final cost. Most of the proposed controllers in the territory of artificial intelligence have complicated computations that make them inapplicable. In this paper particle swarm optimization is used for identification of the DC motor and then adaptive neuro-fuzzy inference system is applied while it trained off-line by PSO. The proposed controller has been implemented in AVR´s ATMEGA32 microcontroller.