Real-time system identification of superconducting cavities with a recursive least-squares algorithm: closed-loop operation

TL;DR

This paper presents a recursive least-squares algorithm for real-time identification of superconducting cavity parameters in closed-loop RF systems, analyzing its convergence and performance for static and dynamic conditions.

Contribution

It introduces a novel recursive least-squares method tailored for real-time cavity parameter estimation in superconducting RF systems, with detailed convergence analysis.

Findings

Effective estimation of cavity bandwidth and detuning in simulations

Convergence properties established for static and time-varying systems

Potential for improved real-time control of superconducting cavities

Abstract

We simulate a recursive least-squares estimator to determine the bandwidth $\omega_{12}$ and the detuning $\Delta\omega$ of a cavity that is controlled with a low-level RF system and we present a comprehensive analysis of the convergence and asymptotic behavior of the algorithm for static and time-varying systems.