Discrete- Time Recurrent Neural Induction Motor Control using Kalman Learning

This paper deals with the adaptive tracking problem for discrete-time induction motor model in presence of bounded disturbances. In this paper, a high order neural network structure is used to identify the plant model and based on this model, a discrete-time control law is derived, which combines discrete-time block control and sliding modes techniques. The paper also includes the respective stability analysis, for the whole system with a strategy to avoid specific adaptive weights zero-crossing. Applicability of the scheme is illustrated via simulation for a discrete-time nonlinear model of an electric induction motor.

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