DocumentCode :
3550834
Title :
Minimum error entropy Luenberger observer
Author :
Xu, Jian-Wu ; Erdogmus, Deniz ; Principe, Jose C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Florida Univ., Gainesville, FL, USA
fYear :
2005
fDate :
8-10 June 2005
Firstpage :
1923
Abstract :
In this paper, we apply the information-theoretic learning (ITL) technique to the extended Luenberger observer. Instead of prespecifying the globally stable observer gains for nonlinear dynamic systems, we propose minimizing the entropy of the error between the measurement and the estimated output to update the observer gains. A stochastic gradient-based algorithm is presented and the performance of the entropy observer is demonstrated on linear and nonlinear dynamic systems. We also point out that this approach leads to the introduction of kernel methods into state estimation.
Keywords :
gradient methods; linear systems; minimum entropy methods; nonlinear control systems; observers; stability; stochastic processes; time-varying systems; information-theoretic learning technique; kernel methods; minimum error entropy Luenberger observer; nonlinear dynamic systems; state estimation; stochastic gradient-based algorithm; Adaptive systems; Control design; Entropy; Equations; Estimation error; Noise measurement; Nonlinear dynamical systems; Observers; State estimation; Stochastic resonance;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
American Control Conference, 2005. Proceedings of the 2005
ISSN :
0743-1619
Print_ISBN :
0-7803-9098-9
Electronic_ISBN :
0743-1619
Type :
conf
DOI :
10.1109/ACC.2005.1470250
Filename :
1470250
Link To Document :
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