Direct transfer of optimized controllers to similar systems using dimensionless MPC

2025-12-09

AI Summary

Controller transfer across dynamically similar yet geometrically scaled systems typically requires laborious, system-specific parameter retuning. Method: This paper proposes a zero-shot, parameter-free transfer method based on nondimensional model predictive control (NMPC). By constructing a nondimensional dynamical model, the approach rigorously preserves dynamic similarity and enables direct cross-scale transfer of closed-loop control performance. Furthermore, it integrates Bayesian optimization with reinforcement learning to jointly optimize controller hyperparameters across multiple scales. Contribution/Results: This work presents the first synergistic co-design of nondimensional MPC and automated hyperparameter optimization, substantially enhancing controller generalizability and deployment efficiency. The method is validated on swing-up control of inverted pendulums and autonomous racing car navigation—achieving seamless transfer without any manual tuning. Open-source implementation is provided.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Intelligent Robots: Learning & Optimization for ROB
• Machine Learning: Optimization

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Direct transfer of controllers to similar systems
• Eliminates need for additional tuning in scaling
• Uses dimensionless MPC for closed-loop performance

Innovation

• Dimensionless MPC enables direct controller transfer
• Automated tuning for closed-loop performance across scales
• Uses data from different scales in parameter optimization