Work

Active Informativity: Online Input Design for Data-Driven Control

Year: 2026

Type: preprint

Abstract: We develop a unified framework for online input design in data-driven control from noisy data. We introduce a performance measure and analyze how such quality measure changes as new data are added. Based on such analysis we propose an online method for selecting inputs that ensures that data quality does not decrease over time. We demonstrate the validity of our approach by formulating six common noisy data-driven control problems in our framework, and by solving numerical examples for three of them.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2609.37683

Authors: Wang, Yishu, Rapisarda, Paolo, Lu, Jianquan, Liu, Yang

Institution: Southeast University

Language: English

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Jiangsu Province

Awards: G5077379007, G7323475582, G8132676078

OpenAlex ID: W7214912468

Cites: 1

Cited by:

Related to: 10