Direct transfer of optimized controllers to similar systems using dimensionless MPC

TL;DR

This paper introduces a dimensionless MPC approach that enables direct transfer of optimized controllers between dynamically similar systems, reducing tuning efforts and leveraging multi-scale data.

Contribution

It presents a novel dimensionless formulation of MPC that allows for direct controller transfer and multi-scale data utilization, enhancing control transferability.

Findings

Controller transfer is successful between similar systems.

Dimensionless formulation reduces tuning complexity.

Multi-scale data improves parameter optimization.

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.