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Input‐to‐state stabilizing sub‐optimal NMPC with an application to DC–DC converters
Author(s) -
Lazar M.,
Heemels W. P. M. H.,
Roset B. J. P.,
Nijmeijer H.,
van den Bosch P. P. J.
Publication year - 2007
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.1249
Subject(s) - control theory (sociology) , robustness (evolution) , converters , nonlinear system , model predictive control , computer science , lyapunov function , affine transformation , mathematical optimization , mathematics , engineering , control (management) , voltage , biochemistry , chemistry , physics , quantum mechanics , artificial intelligence , pure mathematics , electrical engineering , gene
This article focuses on the synthesis of computationally friendly sub‐optimal nonlinear model predictive control (NMPC) algorithms with guaranteed robust stability. To analyse the robustness of the MPC closed‐loop system, we employ the input‐to‐state stability (ISS) framework. To design ISS sub‐optimal NMPC schemes, a new Lyapunov‐based method is proposed. ISS is ensured via a set of constraints, which can be specified as a finite number of linear inequalities for input affine nonlinear systems. Furthermore, the method allows for online optimization over the ISS gain of the resulting closed‐loop system. The potential of the developed theory for the control of fast nonlinear systems, with sampling periods below 1 ms, is illustrated by applying it to control a Buck‐Boost DC–DC converter. Copyright © 2007 John Wiley & Sons, Ltd.