Neural network indirect adaptive control with fast learning algorithm

A fast learning algorithm based on a new cost function and a linearized error signal is proposed. The proposed learning algorithm is applied to indirect adaptive control of nonlinear plants. In the proposed method, we use the identification error and the control error to train the NNI and the NNC, respectively. In addition, we introduce a linearized error signal in order to improve the learning speed. Computer simulation results show that the rate of convergence increases, and that the NNC based on the proposed method is insensitive to variations of the plant parameters.