Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function
In this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the he...
| Main Authors: | , , , |
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| Formato: | Journal article |
| Idioma: | English |
| Publicado: |
Elsevier
2022
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| _version_ | 1826311393617379328 |
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| author | Jahanshahi, H Yao, Q Ijaz Khan, M Moroz, I |
| author_facet | Jahanshahi, H Yao, Q Ijaz Khan, M Moroz, I |
| author_sort | Jahanshahi, H |
| collection | OXFORD |
| description | In this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the help of neural network (NN) and tan-type barrier Lyapunov function (BLF). The NN is introduced to identify the unknown part in the dynamic model of the space manipulator. Benefiting from the neural identification, the proposed controller is model-free and insensitive to external perturbations. Moreover, the BLF is adopted to guarantee the position tracking errors never exceed the predefined output constraints. Different from log-type BLF, the tan-type BLF is employed for the control design, which makes the proposed controller universal for the cases with and without considering the output constraints. The semiglobal uniform ultimate boundedness of the resulting closed-loop system is strictly obtained through stability argument. All error variables in the closed-loop system can eventually stabilize to the small residual sets about zero under the proposed controller. Lastly, simulations and comparisons are given to demonstrate the effectiveness and excellent tracking performance of the proposed control scheme. |
| first_indexed | 2024-03-07T08:09:11Z |
| format | Journal article |
| id | oxford-uuid:2bb9e863-1a36-4bde-9eb6-da5cce9f53dc |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:09:11Z |
| publishDate | 2022 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | oxford-uuid:2bb9e863-1a36-4bde-9eb6-da5cce9f53dc2023-11-15T09:59:16ZUnified neural output-constrained control for space manipulator using tan-type barrier Lyapunov functionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:2bb9e863-1a36-4bde-9eb6-da5cce9f53dcEnglishSymplectic ElementsElsevier2022Jahanshahi, HYao, QIjaz Khan, MMoroz, IIn this paper, a unified neural control scheme is presented for the output-constrained trajectory tracking of space manipulator under unknown parameters and external perturbations. By utilizing the backstepping control technique as the main framework, the proposed controller is developed with the help of neural network (NN) and tan-type barrier Lyapunov function (BLF). The NN is introduced to identify the unknown part in the dynamic model of the space manipulator. Benefiting from the neural identification, the proposed controller is model-free and insensitive to external perturbations. Moreover, the BLF is adopted to guarantee the position tracking errors never exceed the predefined output constraints. Different from log-type BLF, the tan-type BLF is employed for the control design, which makes the proposed controller universal for the cases with and without considering the output constraints. The semiglobal uniform ultimate boundedness of the resulting closed-loop system is strictly obtained through stability argument. All error variables in the closed-loop system can eventually stabilize to the small residual sets about zero under the proposed controller. Lastly, simulations and comparisons are given to demonstrate the effectiveness and excellent tracking performance of the proposed control scheme. |
| spellingShingle | Jahanshahi, H Yao, Q Ijaz Khan, M Moroz, I Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function |
| title | Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function |
| title_full | Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function |
| title_fullStr | Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function |
| title_full_unstemmed | Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function |
| title_short | Unified neural output-constrained control for space manipulator using tan-type barrier Lyapunov function |
| title_sort | unified neural output constrained control for space manipulator using tan type barrier lyapunov function |
| work_keys_str_mv | AT jahanshahih unifiedneuraloutputconstrainedcontrolforspacemanipulatorusingtantypebarrierlyapunovfunction AT yaoq unifiedneuraloutputconstrainedcontrolforspacemanipulatorusingtantypebarrierlyapunovfunction AT ijazkhanm unifiedneuraloutputconstrainedcontrolforspacemanipulatorusingtantypebarrierlyapunovfunction AT morozi unifiedneuraloutputconstrainedcontrolforspacemanipulatorusingtantypebarrierlyapunovfunction |