DocumentCode :
3007613
Title :
Generalized LQG design by filter and controller model selection
Author :
Brehm, Thomas E. ; Maybeck, Peter S.
Author_Institution :
Dept. of Electr. & Comput. Eng., Air Force Inst. of Technol., Wright-Patterson AFB, OH, USA
Volume :
3
fYear :
2000
fDate :
2000
Firstpage :
2700
Abstract :
This paper investigates a generalization of the conventional approach to LQG control design. First we investigate removing the assumption that the Kalman filter as the observer is necessarily based on the same model as the best plant model. The controller gain matrix design is performed as usual, based on the optimal solution to the deterministic design for the best model of the red-world plant. For the next case, we also remove this controller design restriction to investigate robustness to uncertainties in the plant model. The filter and controller gain matrices are both determined by models possibly other than the plant model. We relate the plant model to the filter and controller design models by a position correlation (mean square error on output) measure in order to determine optimal performance
Keywords :
Kalman filters; control system synthesis; filtering theory; linear quadratic Gaussian control; matrix algebra; observers; robust control; Kalman filter; LQG control design; controller design restriction; controller gain matrix design; controller model selection; deterministic design; filter gain matrix; filter selection; generalized LQG design; mean square error; optimal performance determination; optimal solution; position correlation; robustness; uncertainties; Control design; Costs; Error correction; Filters; Observers; Optimal control; Regulators; Riccati equations; State estimation; State feedback;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
ISSN :
0191-2216
Print_ISBN :
0-7803-6638-7
Type :
conf
DOI :
10.1109/CDC.2000.914213
Filename :
914213
Link To Document :
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