Discrete-time nonlinear recurrent high order neural observer

This paper presents the design of an adaptive recurrent neural observer for nonlinear systems, whose mathematical model is assumed to be unknown. The observer is based on a recurrent high order neural network (RHONN), which estimates the state vector of the unknown plant dynamics. The learning algorithm for the RHONN is based on an extended Kalman filter. This paper also includes the respective stability analysis, on the basis of the Lyapunov approach, for the neural observer trained with the extended Kalman filter. Some simulation results are included to illustrate the applicability of the proposed scheme.