Distributed Data-Driven Predictive Control via Dissipative Behavior Synthesis

被引：6

作者：

Yan, Yitao ^{[1
]}

Bao, Jie ^{[1
]}

Huang, Biao ^{[2
]}

机构：

[1] Univ New South Wales Sydney, Sch Chem Engn, Sydney, NSW 2052, Australia

[2] Univ Alberta, Dept Chem & Mat Engn, Edmonton, AB T6G 2R3, Canada

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2024年 / 69卷 / 05期

基金：

澳大利亚研究理事会;

关键词：

Behavioral systems theory; data-driven predictive control; dissipativity; distributed control; QUADRATIC DIFFERENTIAL FORMS; DYNAMICAL-SYSTEMS; STABILITY;

D O I：

10.1109/TAC.2023.3298281

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

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.

引用

页码：2899 / 2914

页数：16