Improved off‐policy reinforcement learning algorithm for robust control of unmodeled nonlinear system with asymmetric state constraints

In this article, an improved data‐based off‐policy reinforcement learning algorithm is proposed for the robust control of unmodeled nonlinear systems with asymmetric state constraints. An improved nonlinear mapping is defined for the asymmetric state constraint problem, which can ensure that the map...

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Vydané v:	International journal of robust and nonlinear control Ročník 33; číslo 3; s. 1607 - 1632
Hlavní autori:	Zhang, Yong, Mu, Chaoxu, Feng, Yanghe, Zhao, Zhijia
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Bognor Regis Wiley Subscription Services, Inc 01.02.2023
Predmet:	Algorithms asymmetric state constraints Asymmetry Control algorithms Control systems design Control theory Data sampling Machine learning Mapping neural networks Nonlinear control Nonlinear systems principal component analysis Principal components analysis reinforcement learning Robust control Sampling methods
ISSN:	1049-8923, 1099-1239
On-line prístup:	Získať plný text
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