Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning
This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal t...
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| Published in: | Complexity (New York, N.Y.) Vol. 2021; no. 1 |
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| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
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Hoboken
Hindawi
2021
John Wiley & Sons, Inc Wiley |
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| ISSN: | 1076-2787, 1099-0526 |
| Online Access: | Get full text |
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| Abstract | This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method. |
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| AbstractList | This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement learning (RL) method. To this end, we rewrite the robotic system model as a state-space form, which will facilitate the realization of optimal tracking control synthesis. To maintain the tracking response, a steady-state control is designed, and then an adaptive optimal tracking control is used to ensure that the tracking error can achieve convergence in an optimal sense. To solve the obtained optimal control via the framework of adaptive dynamic programming (ADP), the command trajectory to be tracked and the modified tracking Hamilton-Jacobi-Bellman (HJB) are all formulated. An online RL algorithm is the developed to address the HJB equation using a critic NN with online learning algorithm. Simulation results are given to verify the effectiveness of the proposed method. |
| Author | Long, Xiaoyi He, Zheng Wang, Zhongyuan |
| Author_xml | – sequence: 1 givenname: Xiaoyi orcidid: 0000-0001-7810-2697 surname: Long fullname: Long, Xiaoyi organization: School of Computer ScienceWuhan UniversityWuhan 430072Chinawhu.edu.cn – sequence: 2 givenname: Zheng orcidid: 0000-0002-7700-0901 surname: He fullname: He, Zheng organization: School of Computer ScienceWuhan UniversityWuhan 430072Chinawhu.edu.cn – sequence: 3 givenname: Zhongyuan orcidid: 0000-0002-9796-488X surname: Wang fullname: Wang, Zhongyuan organization: School of Computer ScienceWuhan UniversityWuhan 430072Chinawhu.edu.cn |
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| Cites_doi | 10.1002/rnc.3247 10.1080/00207721.2014.906681 10.1109/TNNLS.2016.2582849 10.1002/rnc.3018 10.1109/tase.2013.2296206 10.1109/37.980247 10.1080/00207179.2015.1060362 10.1016/j.jfranklin.2019.07.022 10.1016/j.automatica.2014.05.011 10.1016/j.isatra.2019.08.025 10.1016/j.automatica.2012.06.008 10.1016/j.neucom.2020.02.025 10.1109/JAS.2014.7004668 10.1016/j.automatica.2012.09.019 10.1049/iet-cta.2015.0590 10.1016/j.automatica.2012.06.096 10.1109/TSMC.2018.2861826 10.1016/j.automatica.2004.11.034 10.1109/87.553662 10.1007/978-981-13-1712-5_12 10.1002/9780470182963 10.1016/j.neunet.2009.03.008 10.1109/CDC40024.2019.9030116 10.1109/tac.2014.2317301 10.1016/j.automatica.2013.09.043 |
| ContentType | Journal Article |
| Copyright | Copyright © 2021 Xiaoyi Long et al. Copyright © 2021 Xiaoyi Long et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 |
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| DOI | 10.1155/2021/8839391 |
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| Snippet | This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)-based reinforcement... This paper suggests an online solution for the optimal tracking control of robotic systems based on a single critic neural network (NN)‐based reinforcement... |
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| SubjectTerms | Adaptive control Algorithms Design Distance learning Dynamic programming Machine learning Neural networks Optimal control Robot control Robotics Tracking control Tracking errors |
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| Title | Online Optimal Control of Robotic Systems with Single Critic NN-Based Reinforcement Learning |
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