Discrete-Time Backstepping Neural Control for Synchronous Generators

This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of bounded disturbances. In this paper, a high order neural network structure is used to approximate a control law designed by the backstepping technique, applied to a block strict feedback form (BSFF). The learning algorithm for the HONN is based on an extended Kalman filter (EKF). This paper also includes the respective stability analysis, using the Lyapunov approach. The viability of the proposed approach is tested via simulations, by its application to synchronous generators control.

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