Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots
In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2024/1330458 |
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| author | Jun Wang Jiali Zhang Jafar Tavoosi Mohammadamin Shirkhani |
| author_facet | Jun Wang Jiali Zhang Jafar Tavoosi Mohammadamin Shirkhani |
| author_sort | Jun Wang |
| collection | DOAJ |
| description | In this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system. |
| format | Article |
| id | doaj-art-7acadd7c556844a39e9bb331140d8c1b |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-7acadd7c556844a39e9bb331140d8c1b2025-02-03T05:54:34ZengWileyComplexity1099-05262024-01-01202410.1155/2024/1330458Machine Learning-Based Multiagent Control for a Bunch of Flexible RobotsJun Wang0Jiali Zhang1Jafar Tavoosi2Mohammadamin Shirkhani3School of Computer Science and EngineeringSchool of EngineeringDepartment of Electrical EngineeringDepartment of Electrical EngineeringIn this paper, two novel methodologies of employing machine learning (here, the type-2 fuzzy system) are presented to control a multiagent system in which the agents are flexible joint robots. In the previous methods, the static mode controller has been investigated, which has little flexibility and cannot measure all the states of the system, but in the method presented in this paper, we can eliminate these disadvantages. The control signal is consisting of feedback from the output and the estimated states of the system. In the first method, the control signal coefficients are calculated from the linear matrix inequality (LMI), followed by a type-2 fuzzy system that adds the compensation signal to the control signal. In the second method, the type-2 fuzzy system is directly used to estimate the control signal coefficients which do not employ LMI. Both methods have their disadvantages and benefits, so in general, one of these two methods cannot be considered superior. To prove the effectiveness of the two proposed methods, a topology with four robots has been considered. Both proposed methods have been evaluated for controlling the angle and speed of the robot link. Also, another simulation was made without using the fuzzy system to verify the importance of our methods. Simulation results indicate the proper efficiency of proposed methods, especially in presence of uncertainty in the system.http://dx.doi.org/10.1155/2024/1330458 |
| spellingShingle | Jun Wang Jiali Zhang Jafar Tavoosi Mohammadamin Shirkhani Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots Complexity |
| title | Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots |
| title_full | Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots |
| title_fullStr | Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots |
| title_full_unstemmed | Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots |
| title_short | Machine Learning-Based Multiagent Control for a Bunch of Flexible Robots |
| title_sort | machine learning based multiagent control for a bunch of flexible robots |
| url | http://dx.doi.org/10.1155/2024/1330458 |
| work_keys_str_mv | AT junwang machinelearningbasedmultiagentcontrolforabunchofflexiblerobots AT jializhang machinelearningbasedmultiagentcontrolforabunchofflexiblerobots AT jafartavoosi machinelearningbasedmultiagentcontrolforabunchofflexiblerobots AT mohammadaminshirkhani machinelearningbasedmultiagentcontrolforabunchofflexiblerobots |