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Robust stability of nonlinear model predictive control with extended Kalman filter and target setting
Author(s) -
Huang Rui,
Patwardhan Sachin C.,
Biegler Lorenz T.
Publication year - 2012
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.2817
Subject(s) - control theory (sociology) , extended kalman filter , model predictive control , kalman filter , lyapunov function , stability (learning theory) , nonlinear system , computer science , control (management) , artificial intelligence , physics , quantum mechanics , machine learning
SUMMARY This work deals with the closed‐loop robust stability of nonlinear model predictive control (NMPC) coupled with an extended Kalman filter (EKF). First, we point out the gaps between the practical formulations and theoretical research. Then, we show that the estimation error dynamics of an EKF are input‐to‐state stable (ISS) in the presence of nonvanishing perturbations. Moreover, a target setting optimization problem is proposed to solve the target state corresponding to the desired set points, which are used in the objective function in NMPC formulation. Thus, the objective function is a Lyapunov function candidate, and the input‐to‐state practical stability (ISpS) of the closed‐loop system can be established. Moreover, we see that the stability property deteriorates because of the estimation error. Simulation results of the proposed scheme are presented.Copyright © 2012 John Wiley & Sons, Ltd.