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Low Complexity Model Predictive Control

Author(s):

M. Morari
Conference/Journal:

IFAC Symposium on Robust Control Design, (ROCOND'09), The Dan Panorama Hotel Haifa, Haifa, Israel, 16 - 18 June 2009
Abstract:

Parametric programming has received much attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, resulting in a significant decrease in on-line computation effort. I will describe recent work on parametric linear programming (pLP) from the point of view of the control engineer. I will survey various types of algorithms, and identify a new standard convex hull approach that offers significant potential for approximation of pLPs for the purpose of control. The resulting algorithm, based on the beneath/beyond paradigm, computes low-complexity approximate controllers that guarantee stability and feasibility. Many industrial applications will serve to highlight the theoretical developments and the extensive software that helps to bring the theory to bear on the practical examples.
Joint work with Colin Jones and Melanie Zeilinger

Further Information
Year:

2009
Type of Publication:

(05)Plenary/Invited/Honorary Lecture
Supervisor:



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