Adaptive weighting input estimation of a nonlinear system

Abstract

An adaptive and robust input estimation inverse methodology for determining the time-varying unknown force, named as the input, on a nonlinear system is presented. The algorithm employs the extended Kalman filter to propose a regression model between the residual innovation and the forces. Based on this regression equation, a recursive least-squares estimator weighted by an adaptive fading factor is used to estimate the force involving measurement noise and modelling errors on-line. The capabilities of the proposed algorithm are demonstrated by numerical experiments to estimate the exciting forces acting on a nonlinear system, air-damped isolator model. Results show that the proposed method, adaptive weighting input estimation, improves the efficiency and robustness of the conventional input estimation approach. © 2010 Taylor & Francis.