Online learning of data-driven controllers for unknown switched linear systems

TL;DR

This paper introduces an online data-driven control method for unknown switched linear systems that adapts in real-time to changing dynamics, ensuring stability under slow switching conditions.

Contribution

It presents a novel online control design approach that directly uses data without system identification, enabling automatic adaptation to system switches.

Findings

Guarantees stability for slow switching systems

Demonstrates effectiveness in aerospace applications

Provides a real-time adaptive control solution

Abstract

Motivated by the goal of learning controllers for complex systems whose dynamics change over time, we consider the problem of designing control laws for systems that switch among a finite set of unknown discrete-time linear subsystems under unknown switching signals. To this end, we propose a method that uses data to directly design a control mechanism without any explicit identification step. Our approach is online, meaning that the data are collected over time while the system is evolving in closed-loop, and are directly used to iteratively update the controller. A major benefit of the proposed online implementation is therefore the ability of the controller to automatically adjust to changes in the operating mode of the system. We show that the proposed control mechanism guarantees stability of the closed-loop switched linear system provided that the switching is slow enough. Effectiveness of the proposed design technique is illustrated for two aerospace applications.