Direct transfer of optimized controllers to similar systems using dimensionless MPC

• URL: http://arxiv.org/abs/2512.08667v1
• Date: Tue, 09 Dec 2025 14:52:15 GMT
• Title: Direct transfer of optimized controllers to similar systems using dimensionless MPC
• Authors: Josip Kir Hromatko, Shambhuraj Sawant, Šandor Ileš, Sébastien Gros,
• Abstract summary: Scaled model experiments are commonly used in various engineering fields to reduce experimentation costs and overcome constraints associated with full-scale systems.<n>We propose a method to enable a direct controller transfer using dimensionless model predictive control, tuned automatically for closed-loop performance.
• Score: 3.0083198823765565
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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