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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), 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 (910 K)
Document Type: Research Article
DOI: 10.22060/miscj.2017.12143.5005
Authors
Tahereh Gholaminejad 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
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