Open AccessDirect adaptive model predictive control tuning based on the first‐order plus dead time models
Author(s) -
Gholaminejad Tahereh,
KhakiSedigh Ali,
Bagheri Peyman
Publication year - 2017
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2016.1174
Subject(s) - control theory (sociology) , model predictive control , convergence (economics) , multivariable calculus , adaptive control , computer science , controller (irrigation) , stability (learning theory) , heuristic , discrete time and continuous time , dead time , mathematical optimization , control engineering , control (management) , mathematics , engineering , artificial intelligence , machine learning , agronomy , statistics , economics , biology , economic growth
A direct adaptive tuning strategy is proposed for model predictive controllers. Parameter tuning is essential for a satisfactory control performance. Various tuning methods are proposed in the literature which can be categorised as heuristic, numerical and analytical methods. The proposed tuning methodology is based on an analytical model predictive control tuning approach for plants described by first‐order plus dead time models. For a fixed tuning scheme, the tuning performance deteriorates in dealing with unknown or time varying plants. To overcome this problem, an adaptive tuning strategy is utilised. It is suggested to employ a discrete‐time model reference adaptive control with recursive least squares estimations for controller tuning. The proposed method is also extended to multivariable systems. The stability and convergence of the proposed strategy is proved using the Lyapunov approach. Finally, simulation and experimental studies are used to show the effectiveness of the proposed methodology.