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Pole‐zero assignment in model predictive control, using analytical tuning approach
Author(s) -
Bagheri Peyman
Publication year - 2021
Publication title -
optimal control applications and methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.458
H-Index - 44
eISSN - 1099-1514
pISSN - 0143-2087
DOI - 10.1002/oca.2724
Subject(s) - control theory (sociology) , model predictive control , zero (linguistics) , stability (learning theory) , fine tuning , computer science , closed loop , work (physics) , control (management) , control engineering , engineering , physics , artificial intelligence , philosophy , linguistics , quantum mechanics , machine learning , mechanical engineering
Abstract This paper proposes a new analytical tuning strategy for Model Predictive Control (MPC). In the proposed approach, MPC tuning problem is restated as a pole‐zero placement problem. New analytically tuning equations are given and a deep study on the places of poles and zeros of the closed‐loop system is performed. It is known that, appropriate zero placement can improve the robust stability of the closed‐loop system, so the proposed tuning strategy can be applicable. In MPC tuning strategies, analytical equations are very interesting and useful. To achieve analytical equations, First Order plus Dead Time models are used in this work. These models with adequate accuracy can describe many industrial processes. With simulation studies, the effectiveness of the proposed pole‐zero assignment strategy is indicated.