Distributed Data-Driven Predictive Control via Dissipative Behavior Synthesis

• 成果类型: Article
• 署名作者: Yan, Yitao; Bao, Jie; Huang, Biao
• 署名单位: University of New South Wales Sydney; University of Alberta
• 刊物名称: IEEE TRANSACTIONS ON AUTOMATIC CONTROL
• ISSN/ISSBN: 0018-9286
• DOI: 10.1109/TAC.2023.3298281
• 发表日期: 2024
• 页码: 2899-2914
• 关键词: Behavioral systems theory; data-driven predictive control; Dissipativity; Distributed control

摘要

This article presents a distributed data-driven predictive control approach using the behavioral framework. It aims to design a network of controllers for an interconnected system with linear time-invariant subsystems such that a given global (network-wide) cost function is minimized while desired control performance (e.g., network stability and disturbance rejection) is achieved using dissipativity in the quadratic difference form. By viewing dissipativity as a behavior and integrating it into the control design as a virtual dynamical system, the proposed approach carries out the entire design process in a unified framework with a set-theoretic viewpoint. This leads to an effective data-driven distributed control design, where the global design goal can be achieved by distributed optimization based on the local QdF conditions. The approach is illustrated by an example throughout this article.