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A Robust Method for Dual Faceted Linearization
Name
1-s2.0-S2405896320322862-main.pdf
Description
Published version
Size
748.2 KB
Format
Adobe PDF
Checksum (MD5)
aa3660e419f2eb55cfe2dbe59ac4bea8
Author(s)
Igarashi, Yusuke
Yamakita, Masaki
Ng, Jerry
Asada, H Harry
Date Issued
2020
Journal
IFAC-PapersOnLine
Publisher
Elsevier BV
Citation
Igarashi, Yusuke, Yamakita, Masaki, Ng, Jerry and Asada, H Harry. 2020. "A Robust Method for Dual Faceted Linearization." IFAC-PapersOnLine, 53 (2).
Version
Final published version
Abstract
The dynamics of nonlinear systems become linear systems when lifted to higher or infinite dimensional spaces. We call such linear system representations and approximations, ‘lifting linear’ representations. The lifting linear representations are linear system representations that are closer to the original systems than Taylor series approximations. Once we have such a linear system representation, we can apply linear control theory to the nonlinear systems. In Model Predictive Control (MPC), the computation time is reduced because the nonlinear optimization problem becomes a convex quadratic optimization problem. In this paper, we propose a method to make Dual Faceted Linearization (DFL) robust for uncertainties of the plants. It will be shown that the proposed method can yield a lifting linearization leading to better control results for MPC by numerical examples.
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
10.1016/J.IFACOL.2020.12.1683
