Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees

Jamal Arezoo; Karim Salahshoor

doi:doi:10.11648/j.ajcst.20180101.13

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Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees

Jamal Arezoo, Karim Salahshoor

Published in American Journal of Computer Science and Technology (Volume 1, Issue 1)

Received: 21 November 2017 Accepted: 29 November 2017 Published: 24 December 2017

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Abstract

The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.

Published in	American Journal of Computer Science and Technology (Volume 1, Issue 1)
DOI	10.11648/j.ajcst.20180101.13
Page(s)	19-23
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2017. Published by Science Publishing Group

Keywords

Multi-Parametric Programming, Saturated Regions, Search Tree, Predictive Control

References

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[6]	Beccuti, A. G., Papafotiou, G., Frasca, R. and Morari, M. (2007). Explicit hybrid model predictive control of the dc-dcBoost converter. In Power Electronics Specialists Conference, 2007. PESC 2007. IEEE, p. 2503-2509.
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[11]	Storace, M., Repetto, L. and Parodi, M. (2003). A method for the approximate synthesis of cellular non‐linear networks—Part 1: Circuit definition. International Journal of Circuit Theory and Applications, vol. 31, no. 3, p. 277-297.
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APA Style

Jamal Arezoo, Karim Salahshoor. (2017). Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees. American Journal of Computer Science and Technology, 1(1), 19-23. https://doi.org/10.11648/j.ajcst.20180101.13

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ACS Style

Jamal Arezoo; Karim Salahshoor. Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees. Am. J. Comput. Sci. Technol. 2017, 1(1), 19-23. doi: 10.11648/j.ajcst.20180101.13

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AMA Style

Jamal Arezoo, Karim Salahshoor. Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees. Am J Comput Sci Technol. 2017;1(1):19-23. doi: 10.11648/j.ajcst.20180101.13

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@article{10.11648/j.ajcst.20180101.13,
  author = {Jamal Arezoo and Karim Salahshoor},
  title = {Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {19-23},
  doi = {10.11648/j.ajcst.20180101.13},
  url = {https://doi.org/10.11648/j.ajcst.20180101.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.13},
  abstract = {The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.},
 year = {2017}
}

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TY  - JOUR
T1  - Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees
AU  - Jamal Arezoo
AU  - Karim Salahshoor
Y1  - 2017/12/24
PY  - 2017
N1  - https://doi.org/10.11648/j.ajcst.20180101.13
DO  - 10.11648/j.ajcst.20180101.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 19
EP  - 23
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20180101.13
AB  - The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.
VL  - 1
IS  - 1
ER  -

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Author Information

Jamal Arezoo

Department of Automation and Instrumentation, Petroleum University of Technology, Ahvaz, Iran
Karim Salahshoor

Department of Automation and Instrumentation, Petroleum University of Technology, Ahvaz, Iran

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APA Style

Jamal Arezoo, Karim Salahshoor. (2017). Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees. American Journal of Computer Science and Technology, 1(1), 19-23. https://doi.org/10.11648/j.ajcst.20180101.13

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ACS Style

Jamal Arezoo; Karim Salahshoor. Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees. Am. J. Comput. Sci. Technol. 2017, 1(1), 19-23. doi: 10.11648/j.ajcst.20180101.13

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AMA Style

Jamal Arezoo, Karim Salahshoor. Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees. Am J Comput Sci Technol. 2017;1(1):19-23. doi: 10.11648/j.ajcst.20180101.13

Copy | Download

@article{10.11648/j.ajcst.20180101.13,
  author = {Jamal Arezoo and Karim Salahshoor},
  title = {Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees},
  journal = {American Journal of Computer Science and Technology},
  volume = {1},
  number = {1},
  pages = {19-23},
  doi = {10.11648/j.ajcst.20180101.13},
  url = {https://doi.org/10.11648/j.ajcst.20180101.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20180101.13},
  abstract = {The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.},
 year = {2017}
}

Copy | Download

TY  - JOUR
T1  - Complexity Reduction of Explicit Model Predictive Control via Combining Separator Function and Binary Search Trees
AU  - Jamal Arezoo
AU  - Karim Salahshoor
Y1  - 2017/12/24
PY  - 2017
N1  - https://doi.org/10.11648/j.ajcst.20180101.13
DO  - 10.11648/j.ajcst.20180101.13
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 19
EP  - 23
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20180101.13
AB  - The explicit Model Predictive Control (MPC) has emerged as a powerful technique to solve the optimization problem offline for embedded applications where computations is performed online. Despite practical obstacles in implementation of explicit model predictive control (MPC), the main drawbacks of MPC, namely the need to solve a mathematical program on line to compute the control action are removed. This paper addresses complexity of explicit model predictive control (MPC) in terms of online evaluation and memory requirement. Complexity reduction approaches for explicit MPC has recently been emerged as techniques to enhance applicability of MPC. Individual deployment of the approaches has not had enough effect on complexity reduction. In this paper, merging the approaches based on complexity reduction is addressed. The binary search tree and complexity reduction via separation are efficient methods which can be confined to small problems, but merging them can result in significant effect and expansion of its applicability. The simulation tests show proposed approach significantly outperforms previous methods.
VL  - 1
IS  - 1
ER  -

Copy | Download