Position Control of DC Servo Motor Using Neural Network Controller

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In this paper, a class of neural-network controllers with two inputs of error and error change, is applied to the position control of D.C. servo system. The proposed controller is learned by error back-propagating error information to compensate the weighting value using its previous derivatives and to decrease exponentially a series of self learning coefficients. Through the simulations and implementations, the effectiveness and superiority to the conventional fuzzy controller is proved.