Author/Authors
Alex Zheng، نويسنده ,
DocumentNumber
1384305
Title Of Article
Reducing on-line computational demands in model predictive control by approximating QP constraints
شماره ركورد
11369
Latin Abstract
In this paper, we propose two Model Predictive Control algorithms, whose on-line computational demands are signi®cantly
smaller than that for conventional Model Predictive Control algorithms, for control of large-scale constrained linear systems. We
show that closed-loop stability can be guaranteed under some conditions. We also propose an optimal anti-windup scheme for
approximating Model Predictive Control (thus eliminating the need for solving an on-line optimization problem) and derive a
quantitative condition under which Model Predictive Control can be approximated eectively. These results make Model Predictive
Control a very attractive candidate to be applied to systems with small sampling times and/or with a large number of inputs, and
address achievable constrained performance by any anti-windup design.
From Page
279
NaturalLanguageKeyword
Model predictive control , Anti-windup , constrained control , Large scale systems
JournalTitle
Studia Iranica
To Page
290
To Page
290
Link To Document