TY - GEN
T1 - Control of a two-dimensional magnetic positioning system with deep reinforcement learning and feedback linearization
AU - Bejar, Eduardo
AU - Moran, Antonio
N1 - Publisher Copyright:
© 2018 IEEE
PY - 2018/7/2
Y1 - 2018/7/2
N2 - 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.
AB - 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.
KW - Deep deterministic policy gradient algorithm (DDPG)
KW - Magnetic positioning system
KW - Q-learning
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85062234464&partnerID=8YFLogxK
U2 - 10.1109/MWSCAS.2018.8623948
DO - 10.1109/MWSCAS.2018.8623948
M3 - Conference contribution
AN - SCOPUS:85062234464
T3 - Midwest Symposium on Circuits and Systems
SP - 909
EP - 912
BT - 2018 IEEE 61st International Midwest Symposium on Circuits and Systems, MWSCAS 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 61st IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2018
Y2 - 5 August 2018 through 8 August 2018
ER -