@inproceedings{f4104b390cbc4b9986248faa7633e30e,
title = "Model Predictive Control Parameterized in Terms of Orthogonal Polynomials",
abstract = "In this article, model predictive control was parameterized in terms of orthogonal Legendre and Chebyshev polynomials to minimize the set of free variables of constrained optimization problem that has to be solved at every time step. In this study, these orthogonal polynomials were used to approximate the control signal to represent them by a lower dimensional set of variables; these variables are the unknown coefficients of the truncated series of orthogonal polynomials. It was shown that constrained model predictive control and the associated constrained optimization problem can be recast in terms of these unknown coefficients of the truncated series of orthogonal polynomials. This new constrained optimization problem can be solved in a fraction of the time required for the original constrained model predictive control. This new constrained model predictive control was tested on a four-tank bench-marking problem to demonstrate its performance.",
keywords = "Chebyshev polynomial, Constrained optimization, Legendre polynomial, Model Predictive Control, Orthogonal polynomials",
author = "Dogruer, \{Can Ulas\}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th IEEE International Conference on Systems and Control, ICSC 2023 ; Conference date: 18-12-2023 Through 20-12-2023",
year = "2023",
doi = "10.1109/ICSC58660.2023.10449820",
language = "English",
series = "2023 IEEE 11th International Conference on Systems and Control, ICSC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "499--505",
editor = "Driss Mehdi and Maher Kharrat and Mohamed Chaabane and Ahmed El-Hajjaji and Mariem Ghamgui",
booktitle = "2023 IEEE 11th International Conference on Systems and Control, ICSC 2023",
address = "United States",
}