Input-to-state stabilizing sub-optimal NMPC with an application to DC-DC converters

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‐opti...

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Bibliographic Details
Published in:International journal of robust and nonlinear control 2008-05, Vol.18 (8), p.890-904
Main Authors: Lazar, M., Heemels, W. P. M. H., Roset, B. J. P., Nijmeijer, H., van den Bosch, P. P. J.
Format: Article
Language:English
Subjects:
DC-DC converters
input-to-state stability
nonlinear model predictive control
sub-optimality
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Summary: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.
ISSN:1049-8923
1099-1239
DOI:10.1002/rnc.1249