A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning
The large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcem...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/5950678 |
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| author | Jian Sun Jie Li |
| author_facet | Jian Sun Jie Li |
| author_sort | Jian Sun |
| collection | DOAJ |
| description | The large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcement learning control algorithm with the one-layer neural network for each subsystem or called agents to adapt the variation of the networked infrastructures. Each controller includes a critic network and action network for approximating strategy utility function and desired control law, respectively. For avoiding a large number of trials and improving the stability, the training of action network introduces supervised learning mechanisms into reduction of long-term cost. The stability of the control system with learning algorithm is analyzed; the upper bound of the tracking error and neural network weights are also estimated. The effectiveness of our proposed controller is illustrated in the simulation; the results indicate the stability under communication delay and disturbances as well. |
| format | Article |
| id | doaj-art-92ef69eebcaf4544b7ebc7a10235a5ed |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-92ef69eebcaf4544b7ebc7a10235a5ed2025-08-20T03:37:23ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/59506785950678A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement LearningJian Sun0Jie Li1School of Electronic and Information Engineering, Southwest University, Chongqing, ChinaState Grid Chongqing Electric Power Co. Electric Power Research Institute, Chongqing, ChinaThe large scale, time varying, and diversification of physically coupled networked infrastructures such as power grid and transportation system lead to the complexity of their controller design, implementation, and expansion. For tackling these challenges, we suggest an online distributed reinforcement learning control algorithm with the one-layer neural network for each subsystem or called agents to adapt the variation of the networked infrastructures. Each controller includes a critic network and action network for approximating strategy utility function and desired control law, respectively. For avoiding a large number of trials and improving the stability, the training of action network introduces supervised learning mechanisms into reduction of long-term cost. The stability of the control system with learning algorithm is analyzed; the upper bound of the tracking error and neural network weights are also estimated. The effectiveness of our proposed controller is illustrated in the simulation; the results indicate the stability under communication delay and disturbances as well.http://dx.doi.org/10.1155/2018/5950678 |
| spellingShingle | Jian Sun Jie Li A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning Complexity |
| title | A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning |
| title_full | A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning |
| title_fullStr | A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning |
| title_full_unstemmed | A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning |
| title_short | A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning |
| title_sort | stable distributed neural controller for physically coupled networked discrete time system via online reinforcement learning |
| url | http://dx.doi.org/10.1155/2018/5950678 |
| work_keys_str_mv | AT jiansun astabledistributedneuralcontrollerforphysicallycouplednetworkeddiscretetimesystemviaonlinereinforcementlearning AT jieli astabledistributedneuralcontrollerforphysicallycouplednetworkeddiscretetimesystemviaonlinereinforcementlearning AT jiansun stabledistributedneuralcontrollerforphysicallycouplednetworkeddiscretetimesystemviaonlinereinforcementlearning AT jieli stabledistributedneuralcontrollerforphysicallycouplednetworkeddiscretetimesystemviaonlinereinforcementlearning |