A Robust Algorithm for Online Switched System Identification
Zhe Du, Necmiye Ozay, Laura Balzano

TL;DR
This paper introduces a robust online algorithm for identifying switched systems that effectively estimates subsystem parameters and switching sequences with theoretical guarantees and strong empirical performance.
Contribution
A novel two-step online algorithm for switched system identification that improves robustness and provides theoretical convergence guarantees.
Findings
Algorithm outperforms existing methods in simulations.
Effective even with random initialization.
Provides theoretical guarantees on local convergence.
Abstract
In this paper, we consider the problem of online identification of Switched AutoRegressive eXogenous (SARX) systems, where the goal is to estimate the parameters of each subsystem and identify the switching sequence as data are obtained in a streaming fashion. Previous works in this area are sensitive to initialization and lack theoretical guarantees. We overcome these drawbacks with our two-step algorithm: (i) every time we receive new data, we first assign this data to one candidate subsystem based on a novel robust criterion that incorporates both the residual error and an upper bound of subsystem estimation error, and (ii) we use a randomized algorithm to update the parameter estimate of chosen candidate. We provide a theoretical guarantee on the local convergence of our algorithm. Though our theory only guarantees convergence with a good initialization, simulation results show that…
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Taxonomy
TopicsControl Systems and Identification · Fault Detection and Control Systems · Advanced Adaptive Filtering Techniques
