A multilayered neural net controller for servo systems

The authors investigate the possibility of adding a multilayered feedforward neural network controller to an existing servomotor controller to make it an intelligent adaptive controller. The use of the existing controller guarantees coarse learning and thus provides better generalization and correction capabilities. Several learning algorithms are proposed to properly correct the motor inputs under various system nonlinearities, parameter variations over time, and uncertainties. Simulations show very encouraging results. The performance of the proposed controller is compared with that of a proportional-integral-derivative (PID) controller and a model reference adaptive (MRAC) controller