Observer-based neuro identifier

Abstract

A new online identification method is presented. The identified nonlinear systems have partial-state measurement. Their inner states, parameters and structures are unknown. The design is based on the combination of a model-free state observer and a neuro identifier. First, a sliding mode observer, which does not need any information about the nonlinear system, is applied to obtain the full states. A dynamic multilayer neural network is then used to identify the whole nonlinear system. The main contributions of the paper are: a new observer-based identification algorithm is proposed; and a stable learning algorithm for the neuro identifier is given.