论文信息 - Inverse optimal nonlinear recurrent high order neural observer

Inverse optimal nonlinear recurrent high order neural observer

This paper presents the design of an adaptive recurrent neural observer for nonlinear systems which model is assumed to be unknown. The neural observer is composed of a recurrent high order neural network which builds an online model of the unknown plant and a learning adaptation law for the neural network weights. This law is obtained by the Lyapunov methodology. The feedback law which guarantees stability of the estimation error is proved to be optimal with respect to a well defined cost functional.

E.N. Sanchez | L.J. Ricalde

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