IEICE TRANSACTIONS on Fundamentals
Control of Discrete-Time Chaotic Systems with Policy-Based Deep Reinforcement Learning
Summary :
The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a target periodic orbit embedded in its chaotic attractor. Thus, we cannot use this method in the case where a precise mathematical model of the chaotic system cannot be identified. In this case, the delayed feedback control proposed by Pyragas is useful. However, even in the delayed feedback control, we need the mathematical model to determine a feedback gain that stabilizes the periodic orbit. Thus, we propose a reinforcement learning algorithm to the design of a controller for the chaotic system. Recently, reinforcement learning algorithms with deep neural networks have been paid much attention to. Those algorithms make it possible to control complex systems. We propose a controller design method consisting of two steps, where we determine a region including a target periodic point first, and make the controller learn an optimal control policy for its stabilization. The controller efficiently explores its control policy only in the region.
- Publication
- IEICE TRANSACTIONS on Fundamentals Vol.E103-A No.7 pp.885-892
- Publication Date
- 2020/07/01
- Publicized
- Online ISSN
- 1745-1337
- DOI
- 10.1587/transfun.2019EAP1154
- Type of Manuscript
- PAPER
- Category
- Nonlinear Problems
Authors
Junya IKEMOTO
Osaka University
Toshimitsu USHIO
Osaka University
Keyword
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Junya IKEMOTO, Toshimitsu USHIO, "Control of Discrete-Time Chaotic Systems with Policy-Based Deep Reinforcement Learning" in IEICE TRANSACTIONS on Fundamentals,
vol. E103-A, no. 7, pp. 885-892, July 2020, doi: 10.1587/transfun.2019EAP1154.
Abstract: The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a target periodic orbit embedded in its chaotic attractor. Thus, we cannot use this method in the case where a precise mathematical model of the chaotic system cannot be identified. In this case, the delayed feedback control proposed by Pyragas is useful. However, even in the delayed feedback control, we need the mathematical model to determine a feedback gain that stabilizes the periodic orbit. Thus, we propose a reinforcement learning algorithm to the design of a controller for the chaotic system. Recently, reinforcement learning algorithms with deep neural networks have been paid much attention to. Those algorithms make it possible to control complex systems. We propose a controller design method consisting of two steps, where we determine a region including a target periodic point first, and make the controller learn an optimal control policy for its stabilization. The controller efficiently explores its control policy only in the region.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1587/transfun.2019EAP1154/_p
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@ARTICLE{e103-a_7_885,
author={Junya IKEMOTO, Toshimitsu USHIO, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Control of Discrete-Time Chaotic Systems with Policy-Based Deep Reinforcement Learning},
year={2020},
volume={E103-A},
number={7},
pages={885-892},
abstract={The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a target periodic orbit embedded in its chaotic attractor. Thus, we cannot use this method in the case where a precise mathematical model of the chaotic system cannot be identified. In this case, the delayed feedback control proposed by Pyragas is useful. However, even in the delayed feedback control, we need the mathematical model to determine a feedback gain that stabilizes the periodic orbit. Thus, we propose a reinforcement learning algorithm to the design of a controller for the chaotic system. Recently, reinforcement learning algorithms with deep neural networks have been paid much attention to. Those algorithms make it possible to control complex systems. We propose a controller design method consisting of two steps, where we determine a region including a target periodic point first, and make the controller learn an optimal control policy for its stabilization. The controller efficiently explores its control policy only in the region.},
keywords={},
doi={10.1587/transfun.2019EAP1154},
ISSN={1745-1337},
month={July},}
Copy
TY - JOUR
TI - Control of Discrete-Time Chaotic Systems with Policy-Based Deep Reinforcement Learning
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 885
EP - 892
AU - Junya IKEMOTO
AU - Toshimitsu USHIO
PY - 2020
DO - 10.1587/transfun.2019EAP1154
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E103-A
IS - 7
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - July 2020
AB - The OGY method is one of control methods for a chaotic system. In the method, we have to calculate a target periodic orbit embedded in its chaotic attractor. Thus, we cannot use this method in the case where a precise mathematical model of the chaotic system cannot be identified. In this case, the delayed feedback control proposed by Pyragas is useful. However, even in the delayed feedback control, we need the mathematical model to determine a feedback gain that stabilizes the periodic orbit. Thus, we propose a reinforcement learning algorithm to the design of a controller for the chaotic system. Recently, reinforcement learning algorithms with deep neural networks have been paid much attention to. Those algorithms make it possible to control complex systems. We propose a controller design method consisting of two steps, where we determine a region including a target periodic point first, and make the controller learn an optimal control policy for its stabilization. The controller efficiently explores its control policy only in the region.
ER -
English
