This chapter 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 and it has a Luenberger structure. The learning algorithm for the RHONN is implemented using an extended Kaiman filter (EKF). The respective stability analysis, on the basis of the Lyapunov approach, is included for the observer trained with an EKF and simulation results are included to illustrate the applicability of the proposed scheme.
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(2008). Discrete-Time Neural Observers. In: Discrete-Time High Order Neural Control. Studies in Computational Intelligence, vol 112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78289-6_5
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DOI: https://doi.org/10.1007/978-3-540-78289-6_5
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