State Estimation for Nonlinear Systems under Model Uncertainties: A Class of Sliding-Mode Observers

Ricardo Aguilar-López, Universidad Autónoma Metropolitana-Azc
Rafael Maya-Yescas, Universidad Michoacana de San Nicolás de Hidalgo

Abstract

This paper deals with the classical problem of state estimation, considering partially unknown, nonlinear systems with noise 11 measurements. Estimation of both, state variables and unstructured uncertain term, are performed simultaneously. In order to 12 transform the measured disturbance into system disturbance, an alternative system representation is proposed, which lead a more 13 advantageous observer structure. The observer proposed contains a proportional-type contribution and a sliding term for the 14 measurement of error, which provides robustness against noisy measurements and model uncertainties. Convergence analysis of the 15 estimation methodology proposed is performed, analysing the equation of the dynamics of the estimation error; it is shown that the 16 observer exhibits asymptotic convergence. Estimation of monomer concentration, average molecular weight, polydispersity and 17 filtering of temperature in a batch stirred polymerization reactor illustrates the good performance of the observer proposed.