CLC number: TP273
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 4
Clicked: 7068
SU Cheng-li, WANG Shu-qing. Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model[J]. Journal of Zhejiang University Science A, 2006, 7(10): 1723-1732.
@article{title="Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model",
author="SU Cheng-li, WANG Shu-qing",
journal="Journal of Zhejiang University Science A",
volume="7",
number="10",
pages="1723-1732",
year="2006",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2006.A1723"
}
%0 Journal Article
%T Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model
%A SU Cheng-li
%A WANG Shu-qing
%J Journal of Zhejiang University SCIENCE A
%V 7
%N 10
%P 1723-1732
%@ 1673-565X
%D 2006
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2006.A1723
TY - JOUR
T1 - Robust model predictive control for discrete uncertain nonlinear systems with time-delay via fuzzy model
A1 - SU Cheng-li
A1 - WANG Shu-qing
J0 - Journal of Zhejiang University Science A
VL - 7
IS - 10
SP - 1723
EP - 1732
%@ 1673-565X
Y1 - 2006
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2006.A1723
Abstract: An extended robust model predictive control approach for input constrained discrete uncertain nonlinear systems with time-delay based on a class of uncertain T-S fuzzy models that satisfy sector bound condition is presented. In this approach, the minimization problem of the “worst-case” objective function is converted into the linear objective minimization problem involving linear matrix inequalities (LMIs) constraints. The state feedback control law is obtained by solving convex optimization of a set of LMIs. Sufficient condition for stability and a new upper bound on robust performance index are given for these kinds of uncertain fuzzy systems with state time-delay. Simulation results of CSTR process show that the proposed robust predictive control approach is effective and feasible.
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