Model Reference Tracking Control Solutions for a Visual Servo System Based on a Virtual State from Unknown Dynamics

This paper focuses on validating a model-free Value Iteration Reinforcement Learning (MFVI-RL) control solution on a visual servo tracking system in a comprehensive manner starting from theoretical convergence analysis to detailed hardware and software implementation. Learning is based on a virtual...

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書誌詳細

主要な著者:	Timotei Lala, Darius-Pavel Chirla, Mircea-Bogdan Radac
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2021-12-01
シリーズ:	Energies
主題:	reinforcement learning and approximate dynamic programming virtual state feedback reference tuning model reference control unknown dynamics input-output observable system visual servo
オンライン･アクセス:	https://www.mdpi.com/1996-1073/15/1/267
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