Peter Benner, Sabine Hein

Model predictive control based on an LQG design for time-varying linearizations

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Kurzfassung in Englisch

We consider the solution of nonlinear optimal control problems subject to stochastic perturbations with incomplete observations. In particular, we generalize results obtained by Ito and Kunisch in [8] where they consider a receding horizon control (RHC) technique based on linearizing the problem on small intervals. The linear-quadratic optimal control problem for the resulting time-invariant (LTI) problem is then solved using the linear quadratic Gaussian (LQG) design. Here, we allow linearization about an instationary reference trajectory and thus obtain a linear time-varying (LTV) problem on each time horizon. Additionally, we apply a model predictive control (MPC) scheme which can be seen as a generalization of RHC and we allow covariance matrices of the noise processes not equal to the identity. We illustrate the MPC/LQG approach for a three dimensional reaction-diffusion system. In particular, we discuss the benefits of time-varying linearizations over time-invariant ones.

weitere Metadaten

Titel der Schriftenreihe
(Englisch)
Chemnitz Scientific Computing Preprints ; 09-07
Schlagwörter
LTV systems
Schlagwörter
incomplete observations
Schlagwörter
linear quadratic Gaussian design
Schlagwörter
model predictive control
Schlagwörter
noise
Schlagwörter
receding horizon control
SWD SchlagworteNichtlineares System
SWD SchlagworteOptimale Kontrolle
DDC Klassifikation510
Institution(en) 
HochschuleTU Chemnitz
FakultätFakultät für Mathematik
DokumententypPreprint
SpracheEnglisch
Veröffentlichungsdatum (online)11.03.2010
persistente URNurn:nbn:de:bsz:ch1-201000221
ISSN1864-0087
Externe Referenzhttp://www.tu-chemnitz.de/mathematik/csc/preprints.php

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