Stabilizing non-linear MPC using linear parameter-varying representations

Jurre Hanema, Roland Toth, Mircea Lazar

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

8 Citations (Scopus)
2 Downloads (Pure)


We propose a model predictive control approach for non-linear systems based on linear parameter-varying representations. The non-linear dynamics are assumed to be embedded inside an LPV representation. Hence, the non-linear MPC problem is replaced by an LPV MPC problem, which can be solved through convex optimization. Doing so, the non-linear system can be controlled efficiently and with strong guarantees on feasibility and stability at the possible sacrifice of achievable performance. In this paper, the LPV MPC problem is solved using a tube-based approach, requiring the on-line solution of a single linear-or quadratic program. The computational properties of the approach are demonstrated on two examples.

Original languageEnglish
Title of host publication2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Number of pages6
ISBN (Electronic)978-1-5090-2873-3
ISBN (Print)978-1-5090-2874-0
Publication statusPublished - 18 Jan 2018
Event56th IEEE Conference on Decision and Control (CDC 2017) - Melbourne, Australia
Duration: 12 Dec 201715 Dec 2017
Conference number: 56


Conference56th IEEE Conference on Decision and Control (CDC 2017)
Abbreviated titleCDC 2017
Internet address


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