Reducing on-line computational demands in model predictive control by approximating QP constraints

被引:19
作者
Zheng, A [1 ]
机构
[1] Univ Massachusetts, Dept Chem Engn, Amherst, MA 01003 USA
关键词
model predictive control; anti-windup; constrained control; large scale systems;
D O I
10.1016/S0959-1524(99)00005-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this paper, we propose two Model Predictive Control algorithms, whose on-line computational demands are significantly 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 effectively. 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. (C) 1999 Elsevier Science Ltd. All rights reserved.
引用
收藏
页码:279 / 290
页数:12
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