Order and Structural Dependence Selection of LPV-ARX Models using a Nonnegative Garrote Approach
2009 (English)In: Proceedings of the 48th IEEE Conference on Decision and Control held jointly with the 28th Chinese Control Conference, 2009, 7406-7411 p.Conference paper (Refereed)
In order to accurately identify Linear Parameter-Varying (LPV) systems, order selection of LPV linear regression models has prime importance. Existing identification approaches in this context suffer from the drawback that a set of functional dependencies needs to be chosen a priori for the parametrization of the model coefficients. However in a black-box setting, it has not been possible so far to decide which functions from a given set are required for the parametrization and which are not. To provide a practical solution, a nonnegative garrote approach is applied. It is shown that using only a measured data record of the plant, both the order selection and the selection of structural coefficient dependence can be solved by the proposed method.
Place, publisher, year, edition, pages
2009. 7406-7411 p.
ARX, Identification, Linear parameter-varying, Order selection
IdentifiersURN: urn:nbn:se:liu:diva-88426DOI: 10.1109/CDC.2009.5399551ISBN: 978-142443871-6ISBN: 978-1-4244-3872-3OAI: oai:DiVA.org:liu-88426DiVA: diva2:603604
48th IEEE Conference on Decision and Control held jointly with the 28th Chinese Control Conference, Shanghai, China, 15-18 December, 2009