Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning
This study develops an adaptive dynamic programming (ADP) scheme for uncertain systems to achieve the robust trajectory tracking. In this framework, the augmented state is first established via combining the tracking error and reference trajectory, where the robust tracking control problem can be re...
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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/10.1155/2022/8701272 |
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| _version_ | 1832553696347553792 |
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| author | Ziliang Zhao Qinglin Zhu Bin Guo |
| author_facet | Ziliang Zhao Qinglin Zhu Bin Guo |
| author_sort | Ziliang Zhao |
| collection | DOAJ |
| description | This study develops an adaptive dynamic programming (ADP) scheme for uncertain systems to achieve the robust trajectory tracking. In this framework, the augmented state is first established via combining the tracking error and reference trajectory, where the robust tracking control problem can be resolved using the regulation control strategy. Then, the robust control problem of uncertain system can be represented as an optimal control problem of nominal system, which provides a new pathway to address the robust control problem. To realize the optimal control, the derived Hamilton–Jacobi–Bellman equation (HJBE) is solved by training a critic neural network (CNN). Finally, two innovative critic learning techniques are suggested to calculate the unknown NN weights, where the convergence of NN weights can be guaranteed. Simulations are carried out to demonstrate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-8c9c71fc048e489cb5b9eae673c97adb |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-8c9c71fc048e489cb5b9eae673c97adb2025-02-03T05:53:27ZengWileyComplexity1099-05262022-01-01202210.1155/2022/8701272Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic LearningZiliang Zhao0Qinglin Zhu1Bin Guo2College of TransportationCollege of TransportationCollege of TransportationThis study develops an adaptive dynamic programming (ADP) scheme for uncertain systems to achieve the robust trajectory tracking. In this framework, the augmented state is first established via combining the tracking error and reference trajectory, where the robust tracking control problem can be resolved using the regulation control strategy. Then, the robust control problem of uncertain system can be represented as an optimal control problem of nominal system, which provides a new pathway to address the robust control problem. To realize the optimal control, the derived Hamilton–Jacobi–Bellman equation (HJBE) is solved by training a critic neural network (CNN). Finally, two innovative critic learning techniques are suggested to calculate the unknown NN weights, where the convergence of NN weights can be guaranteed. Simulations are carried out to demonstrate the effectiveness of the proposed method.http://dx.doi.org/10.1155/2022/8701272 |
| spellingShingle | Ziliang Zhao Qinglin Zhu Bin Guo Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning Complexity |
| title | Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning |
| title_full | Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning |
| title_fullStr | Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning |
| title_full_unstemmed | Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning |
| title_short | Robust Trajectory Tracking of Uncertain Systems via Adaptive Critic Learning |
| title_sort | robust trajectory tracking of uncertain systems via adaptive critic learning |
| url | http://dx.doi.org/10.1155/2022/8701272 |
| work_keys_str_mv | AT ziliangzhao robusttrajectorytrackingofuncertainsystemsviaadaptivecriticlearning AT qinglinzhu robusttrajectorytrackingofuncertainsystemsviaadaptivecriticlearning AT binguo robusttrajectorytrackingofuncertainsystemsviaadaptivecriticlearning |