Nonlinear Model Predictive Control via Feasibility-Perturbed Sequential Quadratic Programming

被引：0
作者：
Matthew J. Tenny
Stephen J. Wright
James B. Rawlings
机构：
[1] University of Wisconsin,Chemical Engineering Department
[2] University of Wisconsin,Computer Sciences Department
[3] University of Wisconsin,Chemical Engineering Department
来源：
Computational Optimization and Applications | 2004年 / 28卷
关键词：
model predictive control; nonlinear programming; sequential quadratic programming;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Model predictive control requires the solution of a sequence of continuous optimization problems that are nonlinear if a nonlinear model is used for the plant. We describe briefly a trust-region feasibility-perturbed sequential quadratic programming algorithm (developed in a companion report), then discuss its adaptation to the problems arising in nonlinear model predictive control. Computational experience with several representative sample problems is described, demonstrating the effectiveness of the proposed approach.
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页码：87 / 121
页数：34
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