Nonlinear Predictive Control Based on Least Squares Support Vector Machines Hammerstein Models
AbstractThis 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.
|Publication size in sheets||0.5|
|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 English||Process control Model Predictive Control Hammerstein systems Least Squares Support Vector Machines soft computing|
|Score|| = 0.0, 08-01-2020, BookChapterSeriesAndMatConfByIndicator|
= 0.0, 08-01-2020, BookChapterSeriesAndMatConfByIndicator
|Publication indicators||= 4; = 4.0|
|Citation count*||4 (2020-09-09)|
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or Perish system.