Direct identification of continuous-time linear switched state-space models

TL;DR

This paper introduces a direct continuous-time identification algorithm for linear switched state-space models using an integral architecture and coordinate descent optimization, demonstrated through simulation results.

Contribution

It proposes a novel integral architecture-based method for direct CT identification of LSS models, including an optimization algorithm for parameter and state estimation.

Findings

Effective parameter estimation demonstrated in simulation

Accurate switching sequence recovery shown

Algorithm outperforms existing methods in case study

Abstract

This paper presents an algorithm for direct continuous-time (CT) identification of linear switched state-space (LSS) models. The key idea for direct CT identification is based on an integral architecture consisting of an LSS model followed by an integral block. This architecture is used to approximate the continuous-time state map of a switched system. A properly constructed objective criterion is proposed based on the integral architecture in order to estimate the unknown parameters and signals of the LSS model. A coordinate descent algorithm is employed to optimize this objective, which alternates between computing the unknown model matrices, switching sequence and estimating the state variables. The effectiveness of the proposed algorithm is shown via a simulation case study.