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Fast nonlinear MPC for reference tracking subject to nonlinear constraints via quasi-LPV representations
Citation Link: https://doi.org/10.15480/882.3479
Publikationstyp
Conference Paper
Publikationsdatum
2017-10-18
Sprache
English
Institut
TORE-URI
Enthalten in
Volume
50
Issue
1
Start Page
11601
End Page
11606
Citation
IFAC-PapersOnLine 1 (50): 11601-11606 (2017)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Elsevier
This paper presents an approach to efficiently implement Nonlinear Model Predictive Control (NMPC) for reference tracking in the presence of nonlinear input and state constraints by making use of quasi-Linear Parameter Varying (quasi-LPV) representations. Using this framework, standard Quadratic Program (QP) solvers can be used for the online optimization problem, making its solution very efficient and viable even for fast plants. This is an extension of a previous result which considered the regulator problem with input constraints. This approach is tested in a simulation study of a 2-DOF robotic manipulator and its efficiency is compared to that of state-of-the-art NMPC approaches.
Schlagworte
constrained control
efficient algorithms
linear parameter varying systems
Model predictive control
nonlinear systems
DDC Class
600: Technik
Publication version
publishedVersion
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