Discrete-time recurrent neural DC motor control using Kalman learning

Abstract

An adaptive tracking controller for a discrete-time direct current (DC) motor model in presence of bounded disturbances is presented. A high order neural network is used to identify the plant model; this network is trained with an extended Kalman filter. Then, the discrete-time block control and sliding modes techniques are used to develop the reference tracking control. This paper includes also the respective stability analysis and a strategy to avoid specific adaptive weights zero-crossing. The scheme is illustrated via simulations for a discrete-time nonlinear model of an electric DC motor.