Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

Virtual State Feedback Reference Tuning and Value Iteration Reinforcement Learning for Unknown Observable Systems Control

In this paper, a novel Virtual State-feedback Reference Feedback Tuning (VSFRT) and Approximate Iterative Value Iteration Reinforcement Learning (AI-VIRL) are applied for learning linear reference model output (LRMO) tracking control of observable systems with unknown dynamics. For the observable sy...

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Bibliographic Details
Main Authors: Mircea-Bogdan Radac, Anamaria-Ioana Borlea
Format: Article
Language:English
Published: MDPI AG 2021-02-01
Series:Energies
Subjects:
learning control
reference model output tracking
neural networks
state-feedback
reinforcement learning
observability
Online Access:https://www.mdpi.com/1996-1073/14/4/1006

Internet

https://www.mdpi.com/1996-1073/14/4/1006

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