• Home
  • Browse
    • Current Issue
    • By Issue
    • By Author
    • By Subject
    • Author Index
    • Keyword Index
  • Journal Info
    • About Journal
    • Aims and Scope
    • Editorial Board
    • Advisory Board
    • Editorial Staff
    • Publication Ethics
    • Indexing and Abstracting
    • Related Links
    • FAQ
    • Peer Review Process
    • News
  • Guide for Authors
  • Submit Manuscript
  • Reviewers
  • Contact Us
 
  • Login
  • Register
Home Articles List Article Information
  • Save Records
  • |
  • Printable Version
  • |
  • Recommend
  • |
  • How to cite Export to
    RIS EndNote BibTeX APA MLA Harvard Vancouver
  • |
  • Share Share
    CiteULike Mendeley Facebook Google LinkedIn Twitter
AUT Journal of Modeling and Simulation
Articles in Press
Current Issue
Journal Archive
Volume Volume 51 (2019)
Volume Volume 50 (2018)
Issue Issue 2
Issue Issue 1
Volume Volume 49 (2017)
Volume Volume 48 (2016)
Volume Volume 47 (2015)
Volume Volume 46 (2014)
Volume Volume 45 (2013)
Volume Volume 44 (2012)
Volume Volume 43 (2011)
Volume Volume 42 (2010)
Volume Volume 41 (2009)
Gholaminejad, T., Khaki-Sedigh, A., Bagheri, P. (2018). Adaptive Tuning of Model Predictive Control Parameters Based on Analytical Results. AUT Journal of Modeling and Simulation, 50(2), 109-116. doi: 10.22060/miscj.2017.12143.5005
Tahereh Gholaminejad; A. Khaki-Sedigh; P. Bagheri. "Adaptive Tuning of Model Predictive Control Parameters Based on Analytical Results". AUT Journal of Modeling and Simulation, 50, 2, 2018, 109-116. doi: 10.22060/miscj.2017.12143.5005
Gholaminejad, T., Khaki-Sedigh, A., Bagheri, P. (2018). 'Adaptive Tuning of Model Predictive Control Parameters Based on Analytical Results', AUT Journal of Modeling and Simulation, 50(2), pp. 109-116. doi: 10.22060/miscj.2017.12143.5005
Gholaminejad, T., Khaki-Sedigh, A., Bagheri, P. Adaptive Tuning of Model Predictive Control Parameters Based on Analytical Results. AUT Journal of Modeling and Simulation, 2018; 50(2): 109-116. doi: 10.22060/miscj.2017.12143.5005

Adaptive Tuning of Model Predictive Control Parameters Based on Analytical Results

Article 1, Volume 50, Issue 2, Summer and Autumn 2018, Page 109-116  XML PDF (919.07 K)
Document Type: Research Article
DOI: 10.22060/miscj.2017.12143.5005
Authors
Tahereh Gholaminejad email 1; A. Khaki-Sedigh1; P. Bagheri2
1Department of Electrical Engineering, K. N. Toosi University of Technology, Tehran, Iran
2Control Engineering Department Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Abstract
In dealing with model predictive controllers (MPC), controller tuning is a key designing step. Various tuning methods are proposed in the literature which can be categorized as heuristic, numerical and analytical methods. Among the available tuning methods, analytical approaches are more interesting and useful. This paper is based on a proposed analytical MPC tuning approach for plants which can be approximated by first-order plus dead-time models. The performance of such methods fails to deal with unknown or time-varying parameter plants. To overcome this problem, adaptive MPC tuning strategies are practical alternatives. The adaptive MPC tuning approach proposed in this paper is based on on-line identification and analytical tuning formulas. Simulation results are used to show the effectiveness of the proposed methodology. Also, a comparison of the proposed adaptive tuning method with a well-known online tuning method is presented briefly which shows the superiority of the proposed adaptive tuning method.
Keywords
Adaptive model predictive control; analytical tuning; first order plus dead time models
Main Subjects
Control
References

[1] M.L. Darby, M. Nikolaou, MPC: Current practice and challenges, 20(4) (2012) 328-342.

[2] D.Q. Mayne, Model predictive control: Recent developments and future promise, 50(12) (2014) 2967-2986.

[3] J.L. Garriga, M. Soroush, Model predictive control tuning methods: A review, 49(8) (2010) 3505-3515.

[4] P. Bagheri, A. Khaki-Sedigh, Tuning of dynamic matrix controller for FOPDT models using analysis of variance, 44(1) (2011) 12319-12324.

[5] P. Bagheri, A. Khaki-Sedigh, Robust tuning of dynamic matrix controllers for first order plus dead time models, 39(22) (2015) 7017-7031.

[6] P. Bagheri, A. Khaki-Sedigh, K.J.I.C.T. Sedigh, Applications, Analytical approach to tuning of model predictive control for first-order plus dead time models, 7(14) (2013) 1806-1817.

[7] P. Bagheri, A. Khaki-Sedigh, Closed form tuning equations for model predictive control of first-order plus fractional dead time models, 13(1) (2015) 73-80.

[8] P. Bagheri, A. Khaki-Sedigh, An analytical tuning approach to multivariable model predictive controllers, 24(12) (2014) 41-54.

[9] A. Al-Ghazzawi, E. Ali, A. Nouh, E. Zafiriou, On-line tuning strategy for model predictive controllers, 11(3) (2001) 265-284.

[10] E. Ali, A. Al.Ghazzawi, On.line tuning of model predictive controllers using fuzzy logic, 81(5) (2003) 1041-1051.

[11] E. Ali, Heuristic on-line tuning for nonlinear model predictive controllers using fuzzy logic, 13(5) (2003) 383-396.

[12] J. Van der Lee, W. Svrcek, B. Young, A tuning algorithm for model predictive controllers based on genetic algorithms and fuzzy decision making, 47(1) (2008) 53-59.

[13] G. Valencia-Palomo, J. Rossiter, Programmable logic controller implementation of an auto-tuned predictive control based on minimal plant information, 50(1) (2011) 92-100.

[14] Q.N. Tran, J. Scholten, L. Ozkan, A. Backx, A model-free approach for auto-tuning of model predictive control, 47(3) (2014) 2189-2194.

[15] S. Boyd, L. El-Ghaoui, E. Feron, V. Balakrishnan, Linear matrix inequalities in system and control theory, 85(4) (1997) 698-699.

[16] C. Lynch, G. Dumont, Control loop performance monitoring, 4(2) (1996) 185-192.

[17] M.A. Henson, D.E. Seborg, Adaptive nonlinear control of a pH neutralization process, 2(3) (1994) 169-182.

[18] C. Bordons, E.F. Camacho, A generalized predictive controller for a wide class of industrial processes, 6(3) (1998) 372-387.

[19] R. Shridhar, D.J. Cooper, A tuning strategy for unconstrained SISO model predictive control, 36(3) (1997) 729-746.

Statistics
Article View: 1,449
PDF Download: 803
Home | Glossary | News | Aims and Scope | Sitemap
Top Top

AUT Journal of Modeling and Simulation is licensed under a
"Creative Commons Attribution-NonCommercial 2.0 Generic (CC BY-NC 2.0)"

 

Amirkabir University of Technology (Tehran Polytechnic)

Journal Management System. Designed by sinaweb.