Open AccessInput‐to‐state stability of discrete‐time singular systems based on quasi‐min–max model predictive control
Author(s) -
Gao Chan,
Liu XiaoHua,
Li Wuquan
Publication year - 2015
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.2014.1088
Subject(s) - control theory (sociology) , model predictive control , discrete time and continuous time , stability (learning theory) , state (computer science) , mathematics , computer science , control (management) , algorithm , artificial intelligence , statistics , machine learning
This study is concerned with robust quasi‐min–max model predictive control (MPC) for a class of discrete‐time singular systems with persistent disturbance and input constrains. To deal with the persistent disturbance, the authors introduce the notion of input‐to‐state stability (ISS) of discrete‐time singular system for the first time. The optimal control can be obtained by solving a quasi‐min–max optimal problem of a finite horizon cost function. On the basis of the proposed dual‐mode MPC approach, it can be proved that the closed‐loop discrete‐time singular system is ISS, regular and causal. Finally, a numerical simulation shows the feasibility and effectiveness of the proposed method.