Data driven predictive control based on OBF model structures

Alrianes Bachnas, Roland Tóth, Siep Weiland

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

6 Citations (Scopus)
2 Downloads (Pure)


This paper presents a concept of an adaptive model predictive control (MPC) scheme based on a flexible predictor model that utilizes orthonormal basis functions (OBFs). This model structure offers a trade-off between adaptation of the model accuracy in terms of the expansion coefficients and the dynamical structure in terms of the basis functions. We show that this adaptation can maintain desirable control performance. Moreover, since OBF model structures can be seen as a generalization of finite impulse response (FIR) model structures, the incorporation of this scheme in FIR-based MPC is rather straightforward.
Original languageEnglish
Title of host publication54th IEEE Conference on Decision and Control (CDC 2015), 15-18 December 2015, Osaka, Japan
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
ISBN (Electronic)978-1-4799-7885-4
ISBN (Print)978-1-4799-7884-7
Publication statusPublished - Dec 2015
Event54th IEEE Conference on Decision and Control (CDC 2015) - "Osaka International Convention Center", Osaka, Japan
Duration: 15 Dec 201518 Dec 2015
Conference number: 54


Conference54th IEEE Conference on Decision and Control (CDC 2015)
Abbreviated titleCDC 2015
Internet address


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