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arXiv Machine Learning By Ali Reza Daneshvar Garmroodi, Jan Drgo\v{n}a

SD-DPC: Sparse Dictionary Differentiable Predictive Control

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The paper introduces SD‑DPC, a method that learns sparse, interpretable feedback policies for nonlinear systems using data. It first identifies a prediction model via rollout‑based sparse identification of nonlinear dynamics (SINDy), then parameterizes the policy as a sparse combination of dictionary functions and trains it by differentiating a constrained finite‑horizon predictive‑control objective. The resulting explicit feedback law contains only a few terms, satisfies constraints across three benchmark problems, outperforms a distilled policy by up to an order of magnitude, and requires far less memory and online computation than an optimization benchmark while providing explicit sensitivity bounds.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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