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arXiv · 2503.18845

Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection

Joshua Näf·Keith Moffat·Jaap Eising·Florian Dörfler
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Abstract

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.

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Joshua Näf, Keith Moffat, Jaap Eising, Florian Dörfler. 2025-03-24. Choose Wisely: Data-driven Predictive Control for Nonlinear Systems Using Online Data Selection. https://arxiv.org/abs/2503.18845

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