mlsanityData to 14 Oct 2026

Direct transfer of optimized controllers to similar systems using dimensionless MPC

arXiv 2512.08667 ↗First posted 9 Dec 2025eess.SY cs.LG cs.SYcited by 0

Not on the map. Why?

Abstract (arXiv)

Scaled model experiments are commonly used in various engineering fields to reduce experimentation costs and overcome constraints associated with full-scale systems. The relevance of such experiments relies on dimensional analysis and the principle of dynamic similarity. However, transferring controllers to full-scale systems often requires additional tuning. In this paper, we propose a method to enable a direct controller transfer using dimensionless model predictive control, tuned automatically for closed-loop performance. With this reformulation, the closed-loop behavior of an optimized controller transfers directly to a new, dynamically similar system. Additionally, the dimensionless formulation allows for the use of data from systems of different scales during parameter optimization. We demonstrate the method on a cartpole swing-up and a car racing problem, applying either reinforcement learning or Bayesian optimization for tuning the controller parameters. Software used to obtain the results in this paper is publicly available at https://github.com/josipkh/dimensionless-mpcrl.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 32 references that are in the map. First posted on arXiv 9 Dec 2025.

Builds on

14 of 32 references.

References · 14

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  2. 2019Optuna: A Next-generation Hyperparameter Optimization Frameworkcited by 2,501
  3. 2015Contextual Markov Decision Processescited by 220
  4. 2019Data-driven Economic NMPC using Reinforcement Learningcited by 68
  5. 2019acados: a modular open-source framework for fast embedded optimal controlcited by 68
  6. 2023Physics-Informed Machine Learning for Modeling and Control of Dynamical Systemscited by 28
  7. 2025Synthesis of Model Predictive Control and Reinforcement Learning: Survey and Classificationcited by 26
  8. 2019Performance-oriented model learning for data-driven MPC designcited by 22
  9. 2022Dimensionally Consistent Learning with Buckingham Picited by 22
  10. 2020Crossing The Gap: A Deep Dive into Zero-Shot Sim-to-Real Transfer for Dynamicscited by 21
  11. 2020A Data-Driven Automatic Tuning Method for MPC under Uncertainty using Constrained Bayesian Optimizationcited by 16
  12. 2024Reinforced Model Predictive Control via Trust-Region Quasi-Newton Policy Optimizationcited by 4
2 more
  1. 2025Improving Controller Generalization with Dimensionless Markov Decision Processescited by 2
  2. 2025Zero-Shot Policy Transfer in Reinforcement Learning using Buckingham's Pi Theoremcited by 1

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2018Differentiable MPC for End-to-end Planning and Control
  2. 2026Optimal Control with Learned Critics under Unmodeled State Dependencies
  3. 2022Learning to Optimize in Model Predictive Control
  4. 2024Faster Model Predictive Control via Self-Supervised Initialization Learning
  5. 2024Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach
  6. 2022Model Predictive Control via On-Policy Imitation Learning
  7. 2024Safe and Stable Closed-Loop Learning for Neural-Network-Supported Model Predictive Control
  8. 2024A Safe Reinforcement Learning driven Weights-varying Model Predictive Control for Autonomous Vehicle Motion Control