期刊
AUTOMATICA
卷 72, 期 -, 页码 37-45出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.automatica.2016.05.008
关键词
Approximate/adaptive dynamic programming (ADP); Output-feedback control; Nonlinear dynamic uncertainty; Robust optimal control
资金
- U.S. National Science Foundation [ECCS-1501044]
- National Natural Science Foundation of China [61374042]
- Project 111 [B08015]
- Directorate For Engineering
- Div Of Electrical, Commun & Cyber Sys [1501044] Funding Source: National Science Foundation
This paper studies the adaptive and optimal output-feedback problem for continuous-time uncertain systems with nonlinear dynamic uncertainties. Data-driven output-feedback control policies are developed by approximate/adaptive dynamic programming (ADP) based on both policy iteration and value iteration methods. The obtained adaptive and optimal output-feedback controllers differ from the existing literature on the ADP in that they are derived from sampled-data systems theory and are guaranteed to be robust to dynamic uncertainties. A small-gain condition is given under which the overall system is globally asymptotically stable at the origin. An application to power systems is given to test the effectiveness of the proposed approaches. (C) 2016 Elsevier Ltd. All rights reserved.
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