mlsanityData to 14 Oct 2026

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

arXiv 2107.11843 ↗First posted 25 Jul 2021eess.SY cs.LG cs.SYcited by 11

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Abstract (arXiv)

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from the observed system's dynamics, and the neural control law is optimized offline by leveraging the differentiable closed-loop system model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law's parameters directly by backpropagating economic MPC loss through the learned system model. The control performance of the proposed DPC method is demonstrated in simulation using learned model of multi-zone building thermal dynamics.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 11 of its 36 references that are in the map. First posted on arXiv 25 Jul 2021.

Builds on

11 of 36 references.

References · 11

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
  4. 2019Differentiating Through a Cone Programcited by 73
  5. 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 58
  6. 2018Learning an Approximate Model Predictive Controller with Guaranteescited by 57
  7. 2017Imposing Hard Constraints on Deep Networks: Promises and Limitationscited by 43
  8. 2020Physics-constrained Deep Learning of Multi-zone Building Thermal Dynamicscited by 32
  9. 2020Infinite-Horizon Differentiable Model Predictive Controlcited by 17
  10. 2019Safe and Near-Optimal Policy Learning for Model Predictive Control using Primal-Dual Neural Networkscited by 13
  11. 2020Constrained Neural Ordinary Differential Equations with Stability Guaranteescited by 13

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Cited by year

Citing papers per year: 2022: 5; 2023: 3; 2024: 1; 2025: 2.

11 papers.

  1. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  2. 2022Neural Lyapunov Differentiable Predictive Controlcited by 7
  3. 2022Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approachcited by 6
  4. 2023Imitation Learning from Nonlinear MPC via the Exact Q-Loss and its Gauss-Newton Approximationcited by 5
  5. 2022Learning Stochastic Parametric Differentiable Predictive Control Policiescited by 4
  6. 2023Stable Linear Subspace Identification: A Machine Learning Approachcited by 4
  7. 2024Differentiable Predictive Control for Large-Scale Urban Road Networkscited by 1
  8. 2025Data-Driven Adaptive PID Control Based on Physics-Informed Neural Networkscited by 1
  9. 2022Neuro-physical dynamic load modeling using differentiable parametric optimizationcited by 0
  10. 2022Domain-aware Control-oriented Neural Models for Autonomous Underwater Vehiclescited by 0
  11. 2025Safety Reinforced Model Predictive Control (SRMPC): Improving MPC with Reinforcement Learning for Motion Planning in Autonomous Drivingcited by 0