Design of nonlinear observers has received considerable attention since the early development of methods for state estimation. The most popular approach is the extended Kalman filter (EKF) that goes through significant degradation in the presence of unmodeled nonlinearities. For uncertain nonlinear systems, adaptive observers have been introduced to estimate the unknown parameters where no apriori information about the unknown parameters is available. While establishing global results, these approaches are only applicable to systems transformable to output feedback form. Over the recent years, neural network (NN) based identification and estimation schemes have been proposed that relax the assumptions on the system at the price of sacrificing on the global nature of the results. However, most of the NN based adaptive observers in the literature require knowledge of the full dimension of the system, therefore may not be suitable for systems with unmodeled dynamics. A novel approach to nonlinear state estimation, robust to unmodeled dynamics, is proposed from the perspective of augmenting an EKF with an NN based adaptive element.

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