Investigation of Unmeasured Parameters Estimation for Distributed Control Systems

Abstract

The problem of unmeasured parameters estimation for distributed control systems is studied in this paper. The Takagi–Sugeno fuzzy model which can appropriate any nonlinear systems is employed, and based on the model, an observer-based fuzzy H∞ filter which has robustness against time-delay, external noise, and system uncertainties is designed. The sufficient condition for the existence of the desired filter is derived in terms of linear matrix inequalities (LMIs) solutions. Moreover, the underdetermined estimation problem in which the number of sensors available is typically less than the number of state variables to be estimated is specifically addressed. A systematic method is proposed to produce a model tuning parameter vector of appropriate dimension for the estimation of the filter, and the optimal transformation matrix is selected via iterative solution to minimize the estimated error. Finally, a simulation example for turbofan aeroengine is given to illustrate the effectiveness of the proposed method, and the estimated error is less than 2.5%.