A Robust Neural Control Design for Multi-drone Slung Payload Manipulation with Control Contraction Metrics
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909820580265984 |
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| author | Liang, Xinyuan Qian, Longhao Lo, Yi Lok Liu, Hugh H. T. |
| author_facet | Liang, Xinyuan Qian, Longhao Lo, Yi Lok Liu, Hugh H. T. |
| contents | This paper presents a robust neural control design for a three-drone slung payload transportation system to track a reference path under external disturbances. The control contraction metric (CCM) is used to generate a neural exponentially converging baseline controller while complying with control input saturation constraints. We also incorporate the uncertainty and disturbance estimator (UDE) technique to dynamically compensate for persistent disturbances. The proposed framework yields a modularized design, allowing the controller and estimator to perform their individual tasks and achieve a zero trajectory tracking error if the disturbances meet certain assumptions. The stability and robustness of the complete system, incorporating both the CCM controller and the UDE compensator, are presented. Simulations are conducted to demonstrate the capability of the proposed control design to follow complicated trajectories under external disturbances. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_01489 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Robust Neural Control Design for Multi-drone Slung Payload Manipulation with Control Contraction Metrics Liang, Xinyuan Qian, Longhao Lo, Yi Lok Liu, Hugh H. T. Systems and Control Robotics This paper presents a robust neural control design for a three-drone slung payload transportation system to track a reference path under external disturbances. The control contraction metric (CCM) is used to generate a neural exponentially converging baseline controller while complying with control input saturation constraints. We also incorporate the uncertainty and disturbance estimator (UDE) technique to dynamically compensate for persistent disturbances. The proposed framework yields a modularized design, allowing the controller and estimator to perform their individual tasks and achieve a zero trajectory tracking error if the disturbances meet certain assumptions. The stability and robustness of the complete system, incorporating both the CCM controller and the UDE compensator, are presented. Simulations are conducted to demonstrate the capability of the proposed control design to follow complicated trajectories under external disturbances. |
| title | A Robust Neural Control Design for Multi-drone Slung Payload Manipulation with Control Contraction Metrics |
| topic | Systems and Control Robotics |
| url | https://arxiv.org/abs/2510.01489 |