Direct current motor control based on high order neural networks using stochastic estimation

An adaptive discrete-time tracking controller for a direct current (DC) motor with controlled excitation flux is presented. A high order neural network in discrete-time is used to identify the plant model; this network is trained with an extended Kalman filter where the associated state and measurement noises discrete-time covariance matrices are calculated with stochastic estimation. Then, the discrete-time block control and sliding mode techniques are used to develop the trajectory tracking for the angular position of a DC motor with separate winding excitation. Numerical computation presented in this paper shows that the proposed method provides accurate estimation for the covariance matrices associated in the extended Kalman filter.