This work studies the stability properties of Switched AutoRegressive eXogenous (SARX) models subject to arbitrary switching sequences. We provide necessary and sufficient conditions for the arbitrary switching stability of multiple-input, single-output SARX models under nonnegativity constraints, and sufficient-only conditions removing sign constraints. The conditions are equivalentlv formulated on state-space representations of SARX models, due to their influential use in designing control strategies. As an application of the aforementioned results, we propose a novel algorithm for the identification of switched models with stability guarantees via Regression Trees, a powerful machine learning technique.
|Titolo:||On the stability of switched arx models, with an application to learning via regression trees|
|Data di pubblicazione:||2021|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|