Iteratively learning the H∞-norm of multivariable systems applied to model-error-modeling of a vibration isolation system
2013 (English)In: Proceedings of the American Control Conference 2013, American Automatic Control Council , 2013, 6703-6708 p.Conference paper (Refereed)
The aim of this paper is to develop a new data-driven approach for learning the H∞-norm of multivariable systems that can be used for model-error-modeling in robust feedback control. The proposed algorithm only requires iterative experiments on the system. Especially for the multivariable situation that is considered in this paper, these experiments have to be judiciously chosen. The proposed algorithm delivers an estimate of the H ∞-norm of an unknown multivariable system, without the need or explicit construction of a (parametric or non-parametric) model. The results are experimentally demonstrated on model-error-modeling of a multivariable industrial active vibration isolation system. Finally, connections to learning control algorithms are established.
Place, publisher, year, edition, pages
American Automatic Control Council , 2013. 6703-6708 p.
, Proceedings of the American Control Conference, ISSN 0743-1619
Active vibration isolation systems, Data-driven approach, Explicit constructions, Multi variables, Non-parametric, Robust feedback control, Vibration isolation systems
Engineering and Technology
IdentifiersURN: urn:nbn:se:kth:diva-133385ScopusID: 2-s2.0-84883532320ISBN: 978-147990177-7OAI: oai:DiVA.org:kth-133385DiVA: diva2:662014
2013 1st American Control Conference, ACC 2013; Washington, DC; United States; 17 June 2013 through 19 June 2013
QC 201311052013-11-052013-10-312013-11-05Bibliographically approved