LMI relaxations and its application to data-driven control design for switched affine systems

TL;DR

This paper develops a data-driven control approach for switched affine systems using LMI relaxations, enabling control design directly from experimental data without explicit system modeling.

Contribution

It introduces a generic matrix constraint based on data for control design and applies LMI relaxations to develop robust control laws for uncertain switched affine systems.

Findings

Control laws successfully designed from data

Validated on an academic example

Demonstrates effectiveness of LMI relaxations

Abstract

The problem of data-driven control is addressed here in the context of switched affine systems. This class of nonlinear systems is of particular importance when controlling many types of applications in electronic, biology, medicine, etc. Still in the view of practical applications, providing an accurate model for this class of systems can be a hard task, and it might be more relevant to work on data issued from some trajectories obtained from experiments and to deploy a new branch of tools to stabilize the systems that are compatible with the processed data. Following the recent concept of data-driven control design, this paper first presents a generic equivalence lemma that shows a matrix constraint based on data, instead of the system parameter. Then, following the concept of robust hybrid limit cycles for uncertain switched affine systems, robust model-based and then data-driven control laws are designed based on a Lyapunov approach. The proposed results are then illustrated and evaluated on an academic example.