State Estimation via Nonlinear Observer under Model Unobservable Uncertainties
This paper deals with the classical problem of state estimation, considering partially unknown nonlinear systems with noise measurements. Estimation of system’s state variables with observable and unobservable unstructured uncertain terms is performed simultaneously. An alternative system representation is proposed to transform the measured disturbance onto system disturbance, which lead an ad hoc observer structure. The proposed observer contains a proportional-type contribution of the measurement error, which provides robustness against noisy measurements; the observable and unobservable uncertainties are estimated via corrective and predictive estimators. Convergence analysis of the proposed estimation methodology is realized following the dynamic equation of the estimation error; asymptotic convergence is obtained. This estimation scheme is applied to a continuous stirred tank reactor. Numerical simulation illustrates the good performance of the proposed observer
Ricardo Aguilar-López, Sergio Martínez, and Rafael Maya-Yescas. "State Estimation via Nonlinear Observer under Model Unobservable Uncertainties" Techniques and Methodologies for Modelling and Simulation Systems (1st ed). Ed. J. Gil Aluja, F. González Santoyo, B. Flores Romero, J.J. Flores Romero. Morelia: AMSE- International Association for Advancement of Modelling and Simulation, 2005. 100-104.
Available at: http://works.bepress.com/ricardo_aguilar_lopez/13
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