Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916752297820160 |
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| author | Näf, Joshua Moffat, Keith Eising, Jaap Dörfler, Florian |
| author_facet | Näf, Joshua Moffat, Keith Eising, Jaap Dörfler, Florian |
| contents | This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in "trajectory space". Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and verify both norm-based and manifold-embedding-based selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators -- rocket-landing, a robotic arm and cart-pole inverted pendulum swing-up -- comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18845 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection Näf, Joshua Moffat, Keith Eising, Jaap Dörfler, Florian Systems and Control This paper proposes Select-Data-driven Predictive Control (Select-DPC), a new method for controlling nonlinear systems using output-feedback for which data are available but an explicit model is not. At each timestep, Select-DPC employs only the most relevant data to implicitly linearize the dynamics in "trajectory space". Then, taking user-defined output constraints into account, it makes control decisions using a convex optimization. This optimal control is applied in a receding-horizon manner. As the online data-selection is the core of Select-DPC, we propose and verify both norm-based and manifold-embedding-based selection methods. We evaluate Select-DPC on three benchmark nonlinear system simulators -- rocket-landing, a robotic arm and cart-pole inverted pendulum swing-up -- comparing them with standard Data-enabled Predictive Control (DeePC) and Time-Windowed DeePC methods, and find that Select-DPC outperforms both methods. |
| title | Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2503.18845 |