DocumentCode :
1686191
Title :
Proposed framework for applying adaptive critics in real-time realm
Author :
Lendaris, George G. ; Santiago, Roberto A. ; Carroll, Michael S.
Author_Institution :
Dept. of Syst. Sci., Portland State Univ., OR, USA
Volume :
2
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
1796
Lastpage :
1801
Abstract :
Adaptive critics have shown much promise for designing optimal nonlinear controllers in an off-line context. Still, their greatest potential exists in the context Of reconfigurable control, that is, real time controller redesign in response to (substantial) changes in plant dynamics. To accomplish this, a framework is proposed for the application of adaptive critics in real-time control (for those critic methods requiring a model of the plant). The framework is presented in the context of work being done in reconfigurable flight control by the NW Computational Intelligence Lab (NWCIL) at Portland State University. The proposal incorporates recent work (by others) in fast and efficient on-line plant identification, considerations for bounding the computational costs of converging neural networks, and a novel approach (by us) toward the task of assuring system stability during the adaptation process. The potential and limitations of the proposed framework are discussed. It is suggested that with the recent rapid reduction in computational barriers, only certain theoretical issues remain as the central barriers to successful on-line application of the methods
Keywords :
adaptive control; computational complexity; control system CAD; neural nets; nonlinear control systems; optimal control; real-time systems; stability; adaptive critics; fast efficient online plant identification; neural networks; optimal nonlinear controller design; real time controller redesign; reconfigurable control; reconfigurable flight control; stability; Adaptive control; Computational intelligence; Control systems; Design methodology; Neural networks; Optimal control; Piecewise linear approximation; Programmable control; Stability; State-space methods;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on
Conference_Location :
Honolulu, HI
ISSN :
1098-7576
Print_ISBN :
0-7803-7278-6
Type :
conf
DOI :
10.1109/IJCNN.2002.1007791
Filename :
1007791
Link To Document :
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