LAUSR

The Hammerstein model has been successfully used to model various industrial systems, while the parameter estimation of such a model is difficult. In this article, a novel adaptive parameter estimation scheme is proposed for the continuous-time Hammerstein model with an asymmetric dead-zone, which avoids using the immeasurable intermediate variables and system states. First, a continuous piecewise linear neural network is adopted to reformulate the dead-zone dynamics, thus facilitating the derivation of dead-zone characteristic parameters. By applying the K-filter operation, an integrated parametric model of the Hammerstein system with input/output measurements is obtained, which allows separating the observer from the parameter estimation. Hence, two adaptive laws based on the estimation error are given to obtain the estimation of unknown parameters of the dead-zone and the linear subsystem. Then, an observer with the estimated parameters is designed to reconstruct the unknown system states. Theoretical analysis demonstrates that both the observer and estimation errors converge to zero. The validity of the proposed methods is verified by numerical simulations and practical experiments on an articulated manipulator.