System identification with partial-state measurement via dynamic multilayer neural networks

This paper proposes a new online identification method for a class of partial-state measurement nonlinear systems. Only input and output are available, the inner state and the structure are unknown. The design of this paper is based on the combination of the state observer with the neuro identifier. As no information of the nonlinear system can be used, first a model-free high-gain observer is designed to estimate the inner state. Then a dynamic multilayer neural network is used to identify the nonlinear system based on the full observed states. By means of a Lyapunov-like analysis we determine the stable learning algorithms for the observer-based neuro identifier.