Robust on-line nonlinear systems identification using multilayer dynamic neural networks with two-time scales

This paper presents a novel identification method for nonlinear systems including the aspects of fast and slow phenomenon via dynamic multilayer neural networks (NN) with two-time scales. The Lyapunov function and singularly perturbed techniques are used to develop the stable learning procedures for the hidden layers and output layers of the dynamic neural networks model. The proposed learning algorithm is similar to the well-known propagation rule of the multilayer perceptrons but with the novel correction terms which guarantee bounded tracking errors and bounded weights. The passivity approach is used to prove that the proposed neural identifier is robust and avoids the need for persistently exciting (PE) conditions. The effectiveness of the algorithm is illustrated via simulation of an electric DC motor and an induction motor identification.