Meta-learning for model-reference data-driven control

TL;DR

This paper introduces a meta-learning based approach for model-reference control that leverages data from similar plants to improve control design efficiency and performance, especially in low-data scenarios.

Contribution

It proposes a novel direct control design method that combines data from similar plants and existing controllers, enhancing VRFT-like methods with meta-learning principles.

Findings

Achieves control performance comparable to iterative methods.

Retains efficiency of one-shot data-driven control techniques.

Effectively utilizes data from similar plants to improve control design.

Abstract

One-shot direct model-reference control design techniques, like the Virtual Reference Feedback Tuning (VRFT) approach, offer time-saving solutions for the calibration of fixed-structure controllers for dynamic systems. Nonetheless, such methods are known to be highly sensitive to the quality of the available data, often requiring long and costly experiments to attain acceptable closed-loop performance. These features might prevent the widespread adoption of such techniques, especially in low-data regimes. In this paper, we argue that the inherent similarity of many industrially relevant systems may come at hand, offering additional information from plants that are similar (yet not equal) to the system one aims to control. Assuming that this supplementary information is available, we propose a novel, direct design approach that leverages the data from similar plants, the knowledge of controllers calibrated on them, and the corresponding closed-loop performance to enhance model-reference control design. More specifically, by constructing the new controller as a combination of the available ones, our approach exploits all the available priors following a meta-learning philosophy, while ensuring non-decreasing performance. An extensive numerical analysis supports our claims, highlighting the effectiveness of the proposed method in achieving performance comparable to iterative approaches, while at the same time retaining the efficiency of one-shot direct data-driven methods like VRFT.