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Title:

Model-free Incremental Adaptive Dynamic Programming based Approximate Robust Optimal Regulation

Document type:
Zeitschriftenaufsatz
Author(s):
Li, Cong; Wang, Yongchao; Liu, Fangzhou; Liu, Qingchen; Buss, Martin
Abstract:
This article presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), utilizes measured input-state data to allow the design of the approximate optimal incremental control strategy, stabilizing the controlled system incrementally under model uncertainties, environmental disturbances, and input saturation. By leveraging the time delay...     »
Keywords:
incremental adaptive dynamic programming, reinforcement learning, robust optimal regulation, time delay estimation
Journal title:
International Journal of Robust and Nonlinear Control
Year:
2022
Journal volume:
32
Journal issue:
5
Pages contribution:
2662-2682
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:10.1002/rnc.5964
WWW:
https://onlinelibrary.wiley.com/doi/abs/10.1002/rnc.5964
Semester:
WS 21-22
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