Discrete-Time Output Trajectory Tracking by Recurrent High-Order Neural Network Control

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 also includes the respective stability analysis, using the Lyapunov approach, for the whole system, which include the nonlinear plant, the neural observer trained with the EKF and the block controller. Simulation results are included to illustrate the applicability of the proposed scheme

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