A methodology for neural network training for control of drives with nonlinearities

The learning process of a multilayered feedforward neural network involves extracting a desired function from the training data presented through an appropriate training algorithm. To achieve the desired function, the generation of good training data is necessary. A closed-loop methodology for neural network training for control of drives with nonlinearities is presented. Problems associated with the more common open-loop training scheme, and how these are addressed by the proposed closed-loop method, are discussed. An inverse nonlinear control using a neural network (INC/NN), a control strategy which incorporates the neural network for control of nonlinear systems, is described and used to demonstrate the effectiveness of the closed-loop training scheme. Simulation studies and experimental results are presented to verify the improvement achieved by the closed-loop training methodology