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Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models

Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 7824)

Abstract

This paper shortly describes nonlinear Model Predictive Control (MPC) algorithms for Least Squares Support Vector Machines (LS-SVM) Hammerstein models. The model consists of a nonlinear steady-state part in series with a linear dynamic part. A linear approximation of the model for the current operating point or a linear approximation of the predicted output trajectory along an input trajectory is used for prediction. As a result, all algorithms require solving on-line a quadratic programming problem or a series of such problems, unreliable and computationally demanding nonlinear optimisation is not necessary.

Keywords

Process control Model Predictive Control Hammerstein systems Least Squares Support Vector Machines soft computing 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Institute of Control and Computation EngineeringWarsaw University of TechnologyWarsawPoland

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