Data-Enabled Neighboring Extremal: Case Study on Model-Free Trajectory Tracking for Robotic Arm
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866913787289796608 |
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| author | Vahidi-Moghaddam, Amin Zhu, Keyi Zhang, Kaixiang Song, Ziyou Li, Zhaojian |
| author_facet | Vahidi-Moghaddam, Amin Zhu, Keyi Zhang, Kaixiang Song, Ziyou Li, Zhaojian |
| contents | Data-enabled predictive control (DeePC) has recently emerged as a powerful data-driven approach for efficient system controls with constraints handling capabilities. It performs optimal controls by directly harnessing input-output (I/O) data, bypassing the process of explicit model identification that can be costly and time-consuming. However, its high computational complexity, driven by a large-scale optimization problem (typically in a higher dimension than its model-based counterpart--Model Predictive Control), hinders real-time applications. To overcome this limitation, we propose the data-enabled neighboring extremal (DeeNE) framework, which significantly reduces computational cost while preserving control performance. DeeNE leverages first-order optimality perturbation analysis to efficiently update a precomputed nominal DeePC solution in response to changes in initial conditions and reference trajectories. We validate its effectiveness on a 7-DoF KINOVA Gen3 robotic arm, demonstrating substantial computational savings and robust, data-driven control performance. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_07292 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Enabled Neighboring Extremal: Case Study on Model-Free Trajectory Tracking for Robotic Arm Vahidi-Moghaddam, Amin Zhu, Keyi Zhang, Kaixiang Song, Ziyou Li, Zhaojian Robotics Systems and Control Data-enabled predictive control (DeePC) has recently emerged as a powerful data-driven approach for efficient system controls with constraints handling capabilities. It performs optimal controls by directly harnessing input-output (I/O) data, bypassing the process of explicit model identification that can be costly and time-consuming. However, its high computational complexity, driven by a large-scale optimization problem (typically in a higher dimension than its model-based counterpart--Model Predictive Control), hinders real-time applications. To overcome this limitation, we propose the data-enabled neighboring extremal (DeeNE) framework, which significantly reduces computational cost while preserving control performance. DeeNE leverages first-order optimality perturbation analysis to efficiently update a precomputed nominal DeePC solution in response to changes in initial conditions and reference trajectories. We validate its effectiveness on a 7-DoF KINOVA Gen3 robotic arm, demonstrating substantial computational savings and robust, data-driven control performance. |
| title | Data-Enabled Neighboring Extremal: Case Study on Model-Free Trajectory Tracking for Robotic Arm |
| topic | Robotics Systems and Control |
| url | https://arxiv.org/abs/2504.07292 |