Neural network control for DC motor micromaneuvering
Anthony P. Tzes, Pei-Yuan Peng, Chen-Chung Houng · IEEE Transactions on Industrial Electronics · 1995
The application of a neural network controller for compensating the effects induced by the friction in a DC motor micromaneuvering system is considered in this article. A backpropagation neural network operating in the specialized learning mode, using the sign gradient descent algorithm, is employed. The input vector to the neural network controller consists of the time history of the motor angular shaft velocity within a prespecified time window. The on-line training of the neural network is performed in the region of interest of the output domain. The neural network output resembles that of a pulse width modulated controller. The effect of the number of neurons in the input and hidden layers on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.>