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This paper deals with real-time adaptive tracking for discrete-time induction motors in the presence of bounded disturbances. 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-mode techniques. This paper also includes the respective stability analysis for...
This paper presents a discrete-time adaptive neural backstepping control for a double fed induction generator connected to an infinity bus, based on a discrete-time high order neural network (HONN), which is trained with an extended Kalman filter (EFK) algorithm. The discrete-time adaptive neural backstepping control performance is illustrated via simulations.
This paper deals with the discrete-time adaptive output trajectory tracking for induction motors in presence of bounded disturbances. A recurrent high order neural network structure is used to design a nonlinear observer 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...
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...
This paper presents the design of an adaptive controller based on the block control technique, and a new neural observer for a class of MIMO discrete-time nonlinear systems. The observer is based on a recurrent high-order neural network (RHONN), which estimates the state vectors of the unknown plant dynamics. The learning algorithm for the RHONN is based on an extended Kalman filter (EKF). This paper...
In this paper, an adaptive predictive control scheme for the steam generator (or boiler) startup of a fossil power plant is discussed. This scheme is proposed in order to determine the fuel flow required to force the downcomer water temperature and the main steam temperature to track their respective references. The computed control actions are not applied automatically to the plant; instead of, they...
This paper presents the design of an adaptive controller based on the block control technique, and a new neural observer for a class of MIMO discrete-time nonlinear systems. The observer is based on a recurrent high-order neural network (RHONN), which estimates the state vectors of the unknown plant dynamics. The learning algorithm for the RHONN is based on an extended Kalman filter (EKF). This paper...
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