Kernel Methods for Linear Discrete-Time Equations

TL;DR

This paper applies learning theory-based kernel methods to estimate system matrices in linear discrete-time systems, demonstrating their use in stabilization through algebraic Riccati equations with numerical examples.

Contribution

It introduces a novel kernel-based approach for system matrix estimation in linear control systems, integrating learning theory with control design.

Findings

Effective estimation of system matrices using kernel methods.

Successful stabilization via algebraic Riccati equations.

Numerical examples validate the approach.

Abstract

Methods from learning theory are used in the state space of linear dynamical and control systems in order to estimate the system matrices. An application to stabilization via algebraic Riccati equations is included. The approach is illustrated via a series of numerical examples.