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

Maciej Ławryńczuk

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.
Author Maciej Ławryńczuk (FEIT / AK)
Maciej Ławryńczuk,,
- The Institute of Control and Computation Engineering
Pages246-255
Publication size in sheets0.5
Book Tomassini Marco, Antonioni A, Daolio F, Buesser P (eds.): Adaptive and Natural Computing Algorithms, Lecture Notes In Computer Science, vol. 7824, 2013, Springer, ISBN 978-3-642-37212-4, 660 p.
Keywords in EnglishProcess control Model Predictive Control Hammerstein systems Least Squares Support Vector Machines soft computing
DOIDOI:10.1007/978-3-642-37213-1_26
URL http://link.springer.com/chapter/10.1007/978-3-642-37213-1_26
Languageen angielski
Score (nominal)0
Score sourcejournalList
ScoreMinisterial score = 0.0, 08-01-2020, BookChapterSeriesAndMatConfByIndicator
Ministerial score (2013-2016) = 0.0, 08-01-2020, BookChapterSeriesAndMatConfByIndicator
Publication indicators WoS Citations = 4; GS Citations = 4.0
Citation count*4 (2020-09-09)
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* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.
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