Open-/Closed-loop Active Learning for Data-driven Predictive Control

TL;DR

This paper introduces active learning strategies for data-driven control of linear systems, optimizing data collection in open- and closed-loop stages to improve system identification and control performance.

Contribution

It proposes novel open- and closed-loop active learning methods that minimize the admissible system set volume and integrate adaptive predictive control with proven stability.

Findings

Closed-loop active learning reduces data requirements.

The adaptive controller maintains stability and feasibility.

Numerical examples demonstrate improved data efficiency.

Abstract

An important question in data-driven control is how to obtain an informative dataset. In this work, we consider the problem of effective data acquisition of an unknown linear system with bounded disturbance for both open-loop and closed-loop stages. The learning objective is to minimize the volume of the set of admissible systems. First, a performance measure based on historical data and the input sequence is introduced to characterize the upper bound of the volume of the set of admissible systems. On the basis of this performance measure, an open-loop active learning strategy is proposed to minimize the volume by actively designing inputs during the open-loop stage. For the closed-loop stage, a closed-loop active learning strategy is designed to select and learn from informative closed-loop data. The efficiency of the proposed closed-loop active learning strategy is proved by showing that the unselected data cannot benefit the learning performance. Furthermore, an adaptive predictive controller is designed in accordance with the proposed data acquisition approach. The recursive feasibility and the stability of the controller are proved by analyzing the effect of the closed-loop active learning strategy. Finally, numerical examples and comparisons illustrate the effectiveness of the proposed data acquisition strategy.