mlsanityData to 7 Oct 2026

Neural Lyapunov Differentiable Predictive Control

arXiv 2205.10728 ↗First posted 22 May 2022eess.SY cs.LG cs.SYcited by 6

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Abstract (arXiv)

We present a learning-based predictive control methodology using the differentiable programming framework with probabilistic Lyapunov-based stability guarantees. The neural Lyapunov differentiable predictive control (NLDPC) learns the policy by constructing a computational graph encompassing the system dynamics, state and input constraints, and the necessary Lyapunov certification constraints, and thereafter using the automatic differentiation to update the neural policy parameters. In conjunction, our approach jointly learns a Lyapunov function that certifies the regions of state-space with stable dynamics. We also provide a sampling-based statistical guarantee for the training of NLDPC from the distribution of initial conditions. Our offline training approach provides a computationally efficient and scalable alternative to classical explicit model predictive control solutions. We substantiate the advantages of the proposed approach with simulations to stabilize the double integrator model and on an example of controlling an aircraft model.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 25 of its 45 references that are in the map. First posted on arXiv 22 May 2022.

Builds on

25 of 45 references.

References · 25

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13 more
  1. 2020Enforcing robust control guarantees within neural network policiescited by 40
  2. 2019Large Scale Model Predictive Control with Neural Networks and Primal Active Setscited by 28
  3. 2020Infinite-Horizon Differentiable Model Predictive Controlcited by 16
  4. 2018Adaptive MPC for Iterative Taskscited by 15
  5. 2019A Neural Network Architecture to Learn Explicit MPC Controllers from Datacited by 14
  6. 2018MPC-Inspired Neural Network Policies for Sequential Decision Makingcited by 13
  7. 2021Deep Learning Explicit Differentiable Predictive Control Laws for Buildingscited by 11
  8. 2019Probabilistic performance validation of deep learning-based robust NMPC controllerscited by 10
  9. 2020Learning Constrained Adaptive Differentiable Predictive Control Policies With Guaranteescited by 10
  10. 2020Neural Lyapunov Model Predictive Control: Learning Safe Global Controllers from Sub-optimal Examplescited by 5
  11. 2022Learning Stochastic Parametric Differentiable Predictive Control Policiescited by 4
  12. 2020Neural Identification for Controlcited by 3
  13. 2014Practical Comparison of Optimization Algorithms for Learning-Based MPC with Linear Modelscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 3; 2025: 0; 2026: 1.

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. 2024(Un)supervised Learning of Maximal Lyapunov Functionscited by 0
  4. 2024Lyapunov Neural ODE State-Feedback Control Policiescited by 0
  5. 2024CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governorcited by 0
  6. 2026partialCBDs: Differentiable Causal Block Diagramscited by 0