Open AccessEllipsoid invariant set‐based robust model predictive control for repetitive processes with constraints
Author(s) -
Lu Jingyi,
Cao Zhixing,
Gao Furong
Publication year - 2016
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.2015.0969
Subject(s) - control theory (sociology) , model predictive control , iterative learning control , computer science , ellipsoid , controller (irrigation) , stability (learning theory) , robust control , invariant (physics) , set (abstract data type) , robustness (evolution) , control (management) , control engineering , control system , artificial intelligence , mathematics , engineering , machine learning , physics , electrical engineering , astronomy , mathematical physics , programming language , biochemistry , chemistry , gene , agronomy , biology
The idea of combining an iterative learning control with a feedback control under a two‐time dimensional (2D) framework has been widely applied in the control of unconstrained repetitive processes. However, how to extend the method to cover constrained systems remains an issue. In this study, a robust model predictive controller, with an iterative learning control incorporated under the 2D framework, is designed for constrained repetitive processes. This controller is able to explicitly guarantee 2D stability and consistent feasibility, making all choices of tuning parameters feasible to constraints. In this way, complicated tuning procedures are avoided and more freedom for controller design and performance optimisation is allowed. The method is applicable to both stable and unstable systems.