NEURAL-NETWORK CONTROL FOR DC MOTOR MICROMANEUVERING

The application of a neural network controller for compensating the ef fects induced by the friction in a dc motor micromaneuvering system is considered in this article. A backpropagation neural network operatin g in the specialized learning mode, using the sign gradient descent al gorithm, is employed. The input vector to the neural network controlle r consists of the time history of the motor angular shaft velocity wit hin a prespecified time window. The on-line training of the neural net work is performed in the region of interest of the output domain, The neural network output resembles that of a Pulse Width Modulated contro ller. The effect of the number of neurons in the input and hidden laye rs on the transient system response is explored. Experimental studies are presented to indicate the effectiveness of the proposed algorithm.