mlsanityData to 14 Oct 2026

Differentiable Predictive Control with Safety Guarantees: A Control Barrier Function Approach

arXiv 2208.02319 ↗First posted 3 Aug 2022eess.SY cs.LG cs.SYcited by 6

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Abstract (arXiv)

We develop a novel form of differentiable predictive control (DPC) with safety and robustness guarantees based on control barrier functions. DPC is an unsupervised learning-based method for obtaining approximate solutions to explicit model predictive control (MPC) problems. In DPC, the predictive control policy parametrized by a neural network is optimized offline via direct policy gradients obtained by automatic differentiation of the MPC problem. The proposed approach exploits a new form of sampled-data barrier function to enforce offline and online safety requirements in DPC settings while only interrupting the neural network-based controller near the boundary of the safe set. The effectiveness of the proposed approach is demonstrated in simulation.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 20 of its 32 references that are in the map. First posted on arXiv 3 Aug 2022.

Builds on

20 of 32 references.

References · 20

  1. 2019Control Barrier Functions: Theory and Applicationscited by 372
  2. 2018Differentiable MPC for End-to-end Planning and Controlcited by 189
  3. 2018Efficient representation and approximation of model predictive control laws via deep learningcited by 58
  4. 2018Learning an Approximate Model Predictive Controller with Guaranteescited by 57
  5. 2020Enforcing robust control guarantees within neural network policiescited by 40
  6. 2019Control Barrier Functions for Systems with High Relative Degreecited by 31
  7. 2019Large Scale Model Predictive Control with Neural Networks and Primal Active Setscited by 29
  8. 2020Infinite-Horizon Differentiable Model Predictive Controlcited by 17
  9. 2019Near-Optimal Rapid MPC using Neural Networks: A Primal-Dual Policy Learning Frameworkcited by 16
  10. 2019A Neural Network Architecture to Learn Explicit MPC Controllers from Datacited by 14
  11. 2021Deep Learning Explicit Differentiable Predictive Control Laws for Buildingscited by 11
  12. 2020Learning Constrained Adaptive Differentiable Predictive Control Policies With Guaranteescited by 10
8 more
  1. 2019Control Barrier Functions for Mechanical Systems: Theory and Application to Robotic Graspingcited by 8
  2. 2021High-order Barrier Functions: Robustness, Safety and Performance-Critical Controlcited by 8
  3. 2020Integral Control Barrier Functions for Dynamically Defined Control Lawscited by 7
  4. 2021Control Barrier Functions in Sampled-Data Systemscited by 6
  5. 2022A Robust, Multiple Control Barrier Function Framework for Input Constrained Systemscited by 6
  6. 2019Self-triggered Control for Safety Critical Systems using Control Barrier Functionscited by 5
  7. 2021Safe Controller Synthesis with Tunable Input-to-State Safe Control Barrier Functionscited by 5
  8. 2022Koopman-based Differentiable Predictive Control for the Dynamics-Aware Economic Dispatch Problemcited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 1; 2025: 1; 2026: 2.

6 papers.

  1. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  2. 2022Differentiable Safe Controller Design through Control Barrier Functionscited by 8
  3. 2025YANNs: Y-wise Affine Neural Networks for Exact and Efficient Representations of Piecewise Linear Functionscited by 2
  4. 2024Neural Control System for Continuous Glucose Monitoring and Maintenancecited by 0
  5. 2026End-to-End Learning of Safe Optimal Feedback Control in High Dimensions with Control Barrier Function Layerscited by 0
  6. 2026Topological Feasibility Guarantees for Differentiable Predictive Controlcited by 0