A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems
This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/5093277 |
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| _version_ | 1832559161805635584 |
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| author | Chen Zhang Wen Qin Ming-Can Fan Ting Wang Mou-Quan Shen |
| author_facet | Chen Zhang Wen Qin Ming-Can Fan Ting Wang Mou-Quan Shen |
| author_sort | Chen Zhang |
| collection | DOAJ |
| description | This paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady-state performance. |
| format | Article |
| id | doaj-art-a509fa2992a44c1ea38026764195d5b9 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-a509fa2992a44c1ea38026764195d5b92025-02-03T01:30:39ZengWileyComplexity1099-05262022-01-01202210.1155/2022/5093277A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot SystemsChen Zhang0Wen Qin1Ming-Can Fan2Ting Wang3Mou-Quan Shen4College of Electrical Engineering and Control ScienceCollege of Electrical Engineering and Control ScienceSchool of Mathematics and StatisticsCollege of Electrical Engineering and Control ScienceCollege of Electrical Engineering and Control ScienceThis paper proposes an adaptive formation tracking control algorithm optimized by Q-learning scheme for multiple mobile robots. In order to handle the model uncertainties and external disturbances, a desired linear extended state observer is designed to develop an adaptive formation tracking control strategy. Then an adaptive method of sliding mode control parameters optimized by Q-learning scheme is employed, which can avoid the complex parameter tuning process. Furthermore, the stability of the closed-loop control system is rigorously proved by means of matrix properties of graph theory and Lyapunov theory, and the formation tracking errors can be guaranteed to be uniformly ultimately bounded. Finally, simulations are presented to show the proposed algorithm has the advantages of faster convergence rate, higher tracking accuracy, and better steady-state performance.http://dx.doi.org/10.1155/2022/5093277 |
| spellingShingle | Chen Zhang Wen Qin Ming-Can Fan Ting Wang Mou-Quan Shen A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems Complexity |
| title | A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems |
| title_full | A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems |
| title_fullStr | A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems |
| title_full_unstemmed | A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems |
| title_short | A Q-Learning-Based Parameters Adaptive Algorithm for Formation Tracking Control of Multi-Mobile Robot Systems |
| title_sort | q learning based parameters adaptive algorithm for formation tracking control of multi mobile robot systems |
| url | http://dx.doi.org/10.1155/2022/5093277 |
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