Discrete-Time Backstepping Synchronous Generator Stabilisation Using a Neural Observer

This paper deals with adaptive tracking for discrete-time MIMO nonlinear systems in presence of bounded disturbances, based on a neural observer. 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); besides 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 both neural networks is based on an Extended Kalman Filter (EKF). The applicability of the proposed approach is tested, via simulations, by its application to synchronous generators control.