arXiv：cs.LG· Ali Reza Daneshvar Garmroodi, Jan Drgo\v{n}a·· 3 小时前AI 评分30
SD-DPC：稀疏字典可微分预测控制
SD-DPC: Sparse Dictionary Differentiable Predictive Control
AI 导读
研究者提出稀疏字典可微分预测控制（SD-DPC），一种从数据中为非线性系统学习稀疏、可解释反馈策略的框架。该方法先用基于 rollout 的 SINDy 辨识预测模型，再将策略参数化为字典函数的稀疏组合，通过该模型对约束有限时域预测控制目标求导训练，使各项由闭环性能而非模仿已有控制器来筛选。
正文
Abstract:We present sparse dictionary differentiable predictive control (SD-DPC), a framework for learning sparse, interpretable feedback policies for nonlinear systems from data. A prediction model is first identified by rollout-based sparse identification of nonlinear dynamics (SINDy), building on gradient-based and multistep formulations. The policy is then parameterized as a sparse combination of dictionary functions and trained by differentiating a constrained finite-horizon predictive-control objective through this model, so that its terms are selected by closed-loop performance rather than by imitating a previously trained controller. The result is an explicit feedback law with only a handful of terms. Across three benchmark control problems, SD-DPC satisfies the constraints in all test scenarios, outperforms a policy distilled onto the same terms by up to an order of magnitude, and requires orders of magnitude less memory and online computation than an optimization benchmark, while admitting explicit sensitivity bounds.
| Subjects: | Systems and Control (eess.SY); Machine Learning (cs.LG); Optimization and Control (math.OC) |
| Cite as: | arXiv:2610.02466 [eess.SY] |
| (or arXiv:2610.02466v1 [eess.SY] for this version) | |
| https://doi.org/10.48550/arXiv.2610.02466 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Ali Reza Daneshvar Garmroodi [view email]
[v1]
Thu, 1 Oct 2026 20:40:29 UTC (730 KB)
来源：arXiv：cs.LG · arxiv.org