LEARNING RATE ALGORITHM FOR NEURAL-NET BASED DIRECT ADAPTIVE-CONTROL

An algorithm to determine the learning rate for neural net based direc t adaptive control is presented. The proposed learning rate ensures fa st convergence of the adaptive controller and at the same time ensures local stability of the control system. The derivation is based upon e rror linearization in the weight space. The use of the proposed learni ng rate is demonstrated through application on direct self-tuning cont rol of nonlinear plants. Simulation results are also presented to vali date the theoretical findings.