| Issue |
MATEC Web Conf.
Volume 210, 2018
22nd International Conference on Circuits, Systems, Communications and Computers (CSCC 2018)
|
|
|---|---|---|
| Article Number | 02022 | |
| Number of page(s) | 5 | |
| Section | Systems | |
| DOI | https://doi.org/10.1051/matecconf/201821002022 | |
| Published online | 05 October 2018 | |
Convenient optimization strategy implemented in multivariable predictive control
1
Department of Process Control, Tomas Bata University in Zlin, Faculty of Applied Informatics, Nad Stranemi 4511, 76005 Zlin, Czech Republic
2
Department of Mathematics with Didactics, University of Ostrava, Faculty of Education, Mlynska 5, 70103 Ostrava, Czech Republic
* Corresponding author: kubalcik@utb.cz
A significantly important part of model predictive control (MPC) with constraints is a solution of an optimization task. The result of the optimization is a vector of future increments of a manipulated variable. The first element of this vector is applied in the next sampling period of MPC in the framework of a receding horizon strategy. In practical realization of a multivariable MPC, the optimization is characterized by higher computational complexity. Therefore, reduction of the computational complexity of the optimization methods has been widely researched. One suitable principle of precomputing operations was proposed by Wang, L. This general optimization strategy is further modified in this paper. Decreasing of the computational complexity of the optimization by using of the proposed modification is discussed.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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