A Neuro-augmented Observer for a Class of Nonlinear Systems

A new type of state observer for nonlinear systems is presented in this paper. This observer is a hybrid of linear and nonlinear parts: it is based on a conventional linear observer design, and augmented by a neural network. The neural network approximates only the nonlinear part of the system. The state estimation error is proved to approach zero asymptotically.