Data-Driven Decentralized Control Design for Discrete-Time Large-Scale Systems

TL;DR

This paper introduces a data-driven method for designing decentralized controllers for large-scale discrete-time systems, eliminating the need for extensive modeling by using subsystem data and Lyapunov-based semi-definite programming.

Contribution

It develops a novel data-driven framework for decentralized control design that bypasses traditional modeling, applicable to large-scale systems with subsystem data.

Findings

Successfully applied to a mass-spring chain model

Avoids extensive system modeling processes

Provides decentralized stabilizing controllers

Abstract

In this paper, a data-driven approach is developed for controller design for a class of discrete-time large-scale systems, where a large-scale system can be expressed in an equivalent data-driven form and the decentralized controllers can be parameterized by the data collected from its subsystems, i.e., system state, control input, and interconnection input. Based on the developed data-driven method and the Lyapunov approach, a data-driven semi-definite programming problem is constructed to obtain decentralized stabilizing controllers. The proposed approach has been validated on a mass-spring chain model, with the significant advantage of avoiding extensive modeling processes.