Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network
IntroductionSince tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.MethodsA new fractional exponential activ...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-09-01
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| Series: | Frontiers in Neurorobotics |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1242063/full |
| _version_ | 1797679269704368128 |
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| author | Yuxuan Cao Boyun Liu Jinyun Pu |
| author_facet | Yuxuan Cao Boyun Liu Jinyun Pu |
| author_sort | Yuxuan Cao |
| collection | DOAJ |
| description | IntroductionSince tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.MethodsA new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.Results and discussionNumerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment. |
| first_indexed | 2024-03-11T23:12:05Z |
| format | Article |
| id | doaj.art-0187618c933048f39676e3350ef5edfd |
| institution | Directory Open Access Journal |
| issn | 1662-5218 |
| language | English |
| last_indexed | 2024-03-11T23:12:05Z |
| publishDate | 2023-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj.art-0187618c933048f39676e3350ef5edfd2023-09-21T07:16:29ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182023-09-011710.3389/fnbot.2023.12420631242063Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural networkYuxuan CaoBoyun LiuJinyun PuIntroductionSince tracked mobile robot is a typical non-linear system, it has been a challenge to achieve the trajectory tracking of tracked mobile robots. A zeroing neural network is employed to control a tracked mobile robot to track the desired trajectory.MethodsA new fractional exponential activation function is designed in this study, and the implicit derivative dynamic model of the tracked mobile robot is presented, termed finite-time convergence zeroing neural network. The proposed model is analyzed based on the Lyapunov stability theory, and the upper bound of the convergence time is given. In addition, the robustness of the finite-time convergence zeroing neural network model is investigated under different error disturbances.Results and discussionNumerical experiments of tracking an eight-shaped trajectory are conducted successfully, validating the proposed model for the trajectory tracking problem of tracked mobile robots. Comparative results validate the effectiveness and superiority of the proposed model for the kinematical resolution of tracked mobile robots even in a disturbance environment.https://www.frontiersin.org/articles/10.3389/fnbot.2023.1242063/fulltracked mobile robottrajectory trackingfinite-time convergencezeroing neural networkrobust |
| spellingShingle | Yuxuan Cao Boyun Liu Jinyun Pu Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network Frontiers in Neurorobotics tracked mobile robot trajectory tracking finite-time convergence zeroing neural network robust |
| title | Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network |
| title_full | Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network |
| title_fullStr | Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network |
| title_full_unstemmed | Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network |
| title_short | Robust control for a tracked mobile robot based on a finite-time convergence zeroing neural network |
| title_sort | robust control for a tracked mobile robot based on a finite time convergence zeroing neural network |
| topic | tracked mobile robot trajectory tracking finite-time convergence zeroing neural network robust |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2023.1242063/full |
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