Title
Control of a two-dimensional magnetic positioning system with deep reinforcement learning and feedback linearization
Date Issued
22 January 2019
Access level
metadata only access
Resource Type
conference paper
Author(s)
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
This paper presents a neuro-controller based on deep reinforcement learning to control the nonlinear dynamics of a two-dimensional magnetic positioning system. The feedback-linearized model of the magnetic positioning system is used to generate training data for the neuro-controller. The neuro-controller is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm. The effectiveness of the proposed control strategy is verified with different desired setpoints and trajectories, and diverse working conditions.
Start page
909
End page
912
Volume
2018-August
Language
English
OCDE Knowledge area
Ingeniería industrial
Sistemas de automatización, Sistemas de control
Subjects
Scopus EID
2-s2.0-85062234464
ISBN
9781538673928
Source
Midwest Symposium on Circuits and Systems
Resource of which it is part
Midwest Symposium on Circuits and Systems
ISSN of the container
15483746
ISBN of the container
978-153867392-8
Conference
61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018
Sources of information:
Directorio de Producción Científica
Scopus