Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions
This paper presents a decentralized zero-sum optimal control method for MRMs with environmental collisions via an actor-critic-identifier (ACI) structure-based adaptive dynamic programming (ADP) algorithm. The dynamic model of the MRMs is formulated via a novel collision identification method that i...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8758094/ |
| _version_ | 1819073655972298752 |
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| author | Bo Dong Tianjiao An Fan Zhou Keping Liu Weibo Yu Yuanchun Li |
| author_facet | Bo Dong Tianjiao An Fan Zhou Keping Liu Weibo Yu Yuanchun Li |
| author_sort | Bo Dong |
| collection | DOAJ |
| description | This paper presents a decentralized zero-sum optimal control method for MRMs with environmental collisions via an actor-critic-identifier (ACI) structure-based adaptive dynamic programming (ADP) algorithm. The dynamic model of the MRMs is formulated via a novel collision identification method that is deployed for each joint module, in which the local position and torque information are used to design the model compensation controller. A neural network (NN) identifier is developed to compensate the model uncertainties and then, the optimal control problem of the MRMs with environmental collisions can be transformed into a two-player zero-sum optimal control one. Based on the ADP algorithm, the Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing the actor-critic NNs, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotically stable. Finally, the experiments are conducted to verify the effectiveness and advantages of the proposed method. |
| first_indexed | 2024-12-21T17:57:05Z |
| format | Article |
| id | doaj.art-4245bf84974744e8a73a24676685443d |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-21T17:57:05Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4245bf84974744e8a73a24676685443d2022-12-21T18:55:10ZengIEEEIEEE Access2169-35362019-01-017961489616510.1109/ACCESS.2019.29275118758094Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental CollisionsBo Dong0Tianjiao An1Fan Zhou2Keping Liu3Weibo Yu4Yuanchun Li5https://orcid.org/0000-0002-0989-6312Department of Control Science and Engineering, Changchun University of Technology, Changchun, ChinaDepartment of Control Science and Engineering, Changchun University of Technology, Changchun, ChinaDepartment of Control Science and Engineering, Changchun University of Technology, Changchun, ChinaDepartment of Control Science and Engineering, Changchun University of Technology, Changchun, ChinaDepartment of Control Science and Engineering, Changchun University of Technology, Changchun, ChinaDepartment of Control Science and Engineering, Changchun University of Technology, Changchun, ChinaThis paper presents a decentralized zero-sum optimal control method for MRMs with environmental collisions via an actor-critic-identifier (ACI) structure-based adaptive dynamic programming (ADP) algorithm. The dynamic model of the MRMs is formulated via a novel collision identification method that is deployed for each joint module, in which the local position and torque information are used to design the model compensation controller. A neural network (NN) identifier is developed to compensate the model uncertainties and then, the optimal control problem of the MRMs with environmental collisions can be transformed into a two-player zero-sum optimal control one. Based on the ADP algorithm, the Hamilton-Jacobi-Isaacs (HJI) equation is solved by constructing the actor-critic NNs, thus making the derivation of the approximate optimal control policy feasible. Based on the Lyapunov theory, the closed-loop robotic system is proved to be asymptotically stable. Finally, the experiments are conducted to verify the effectiveness and advantages of the proposed method.https://ieeexplore.ieee.org/document/8758094/Adaptive dynamic programmingcollision identificationdecentralized optimal controlmodular robot manipulatorszero-sum game |
| spellingShingle | Bo Dong Tianjiao An Fan Zhou Keping Liu Weibo Yu Yuanchun Li Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions IEEE Access Adaptive dynamic programming collision identification decentralized optimal control modular robot manipulators zero-sum game |
| title | Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions |
| title_full | Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions |
| title_fullStr | Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions |
| title_full_unstemmed | Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions |
| title_short | Actor-Critic-Identifier Structure-Based Decentralized Neuro-Optimal Control of Modular Robot Manipulators With Environmental Collisions |
| title_sort | actor critic identifier structure based decentralized neuro optimal control of modular robot manipulators with environmental collisions |
| topic | Adaptive dynamic programming collision identification decentralized optimal control modular robot manipulators zero-sum game |
| url | https://ieeexplore.ieee.org/document/8758094/ |
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