Model-Free control performance improvement using virtual reference feedback tuning and reinforcement Q-learning

2016 ◽  
Vol 48 (5) ◽  
pp. 1071-1083 ◽  
Author(s):  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Raul-Cristian Roman
Keyword(s):  
Performance Improvement ◽  
Control Performance ◽  
Virtual Reference ◽  
Q Learning ◽  
Model Free ◽  
Model Free Control

scholarly journals Gaussian Process Based Model-free Control with Q-Learning

2019 ◽  
Vol 52 (11) ◽  
pp. 236-243
Author(s):  
Jan Hauser ◽  
Daniel Pachner ◽  
Vladimír Havlena
Keyword(s):  
Gaussian Process ◽  
Q Learning ◽  
Model Free ◽  
Model Free Control

scholarly journals Virtual Reference Feedback Tuning of Model-Free Control Algorithms for Servo Systems

Machines ◽  
2017 ◽  
Vol 5 (4) ◽  
pp. 25 ◽  
Author(s):  
Raul-Cristian Roman ◽  
Mircea-Bogdan Radac ◽  
Radu-Emil Precup ◽  
Emil Petriu
Keyword(s):  
Control Algorithms ◽  
Virtual Reference ◽  
Servo Systems ◽  
Model Free ◽  
Model Free Control

Adaptive Optimal Control of a Grid-Independent Photovoltaic System

Solar Energy ◽  
2002 ◽  
Author(s):  
Gregor P. Henze ◽  
Robert H. Dodier
Keyword(s):  
Optimal Control ◽  
Control Strategy ◽  
Learning Algorithm ◽  
Photovoltaic System ◽  
Control Performance ◽  
Adaptive Optimal Control ◽  
Q Learning ◽  
Model Free ◽  
Base Load ◽  
Better Than

This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.

Adaptive Optimal Control of a Grid-Independent Photovoltaic System

2003 ◽  
Vol 125 (1) ◽  
pp. 34-42 ◽  
Author(s):  
Gregor P. Henze ◽  
Robert H. Dodier
Keyword(s):  
Optimal Control ◽  
Control Algorithm ◽  
Learning Algorithm ◽  
Photovoltaic System ◽  
Control Performance ◽  
Adaptive Optimal Control ◽  
Q Learning ◽  
Model Free ◽  
Base Load ◽  
Better Than

This paper investigates adaptive optimal control of a grid-independent photovoltaic system consisting of a collector, storage, and a load. The control algorithm is based on Q-Learning, a model-free reinforcement learning algorithm, which optimizes control performance through exploration. Q-Learning is used in a simulation study to find a policy which performs better than a conventional control strategy with respect to a cost function which places more weight on meeting a critical base load than on those non-critical loads exceeding the base load.

scholarly journals Author Correction: Reliance on model-based and model-free control in obesity

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Lieneke K. Janssen ◽  
Florian P. Mahner ◽  
Florian Schlagenhauf ◽  
Lorenz Deserno ◽  
Annette Horstmann
Keyword(s):  
Model Based ◽  
Model Free ◽  
Model Free Control

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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