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.
Original language | English (US) |
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Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
Pages | 1796-1801 |
Number of pages | 6 |
Volume | 2 |
State | Published - Jan 1 2002 |
Event | 2002 International Joint Conference on Neural Networks (IJCNN '02) - Honolulu, HI, United States Duration: May 12 2002 → May 17 2002 |
Other
Other | 2002 International Joint Conference on Neural Networks (IJCNN '02) |
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Country/Territory | United States |
City | Honolulu, HI |
Period | 5/12/02 → 5/17/02 |
ASJC Scopus subject areas
- Software
- Artificial Intelligence