Obtaining a Stabilizing Prediction Horizon in Quadratic Programming Model Predictive Control

被引：0

作者：

Morgenstern, Dimitri ^{[1
]}

Goerges, Daniel ^{[2
]}

Wirsen, Andreas ^{[1
]}

机构：

[1] Fraunhofer Inst Ind Math, Dept Syst Anal Prognosis & Control, Kaiserslautern, Germany

[2] Univ Kaiserslautern, Dept Elect & Comp Engn, Electromobil, Kaiserslautern, Germany

来源：

2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC) | 2019年

关键词：

FEASIBILITY; PERFORMANCE; MPC;

D O I：

暂无

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, it is shown how a performance tuple can be obtained in model predictive control if the optimal control problem is a quadratic program. The quotient of the finite-horizon optimal cost and the tuple's first entry upper bounds the sum of all instances over the finite-horizon optimal cost. The tuple's second entry is a stabilizing prediction horizon. The algorithm taking the describing matrices and giving a performance tuple is easily verifiable.

引用

页码：463 / 467

页数：5

共 9 条

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