An adaptive neural control of a DC motor

Abstract

A recurrent trainable neural network (RTNN) together with a backpropagation trough-time learning algorithm are applied for a real-time identification and adaptive control of a DC-motor drive. The paper proposes to use three RTNNs separately for the parts of the systems identification, the state feedback control and the feedforward control. The applied RTNN model has a minimum number of parameters due to its Jordan canonical structure, which permits to use the generated vector of states directly for a DC-motor feedback control. The experimental results, confirm the applicability of the described identification and control methodology in practice and also confirm the good quality of the RTNN.