SD-DPC: Sparse Dictionary Differentiable Predictive Control
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Abstract (arXiv)
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.
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References · 11
- 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,079
- 2019Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networkscited by 293
- 2017Sparse identification of nonlinear dynamics for model predictive control in the low-data limitcited by 140
- 2021Ensemble-SINDy: Robust sparse model discovery in the low-data, high-noise limit, with active learning and controlcited by 137
- 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 58
- 2018Learning an Approximate Model Predictive Controller with Guaranteescited by 57
- 2020Automatic Differentiation to Simultaneously Identify Nonlinear Dynamics and Extract Noise Probability Distributions from Datacited by 37
- 2024SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learningcited by 26
- 2020Deep Learning Alternative to Explicit Model Predictive Control for Unknown Nonlinear Systemscited by 22
- 2023Efficient Symbolic Policy Learning with Differentiable Symbolic Expressioncited by 10
- 2024Parameter-Adaptive Approximate MPC: Tuning Neural-Network Controllers without Retrainingcited by 7
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