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Volume Volume 50 (2018)
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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), 1-10. doi: 10.22060/miscj.2017.12143.5005
Tahereh Gholaminejad; Ali Khaki-Sedigh; Peyman Bagheri. "Adaptive Tuning of Model Predictive Control Parameters based on Analytical Results". AUT Journal of Modeling and Simulation, 50, 2, 2018, 1-10. 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. 1-10. 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): 1-10. 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 1-10  XML
Document Type: Research Article
DOI: 10.22060/miscj.2017.12143.5005
Authors
Tahereh Gholaminejad 1; Ali Khaki-Sedigh1; Peyman Bagheri2
1K.N. Toosi University of Technology
2University of Tabriz
Abstract
In dealing with model predictive controllers (MPC), controller tuning is a key design 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 can be approximated by first order plus dead time models. The performance of such methods deteriorates in dealing 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 superiority of the proposed adaptive tuning method.
Keywords
Adaptive model predictive control; analytical tuning; first order plus dead time models
Main Subjects
Control
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