Stochastic Tubes in Model Predictive Control With Probabilistic Constraints

被引：180
|
作者：
Cannon, Mark [1 ]
Kouvaritakis, Basil [1 ]
Rakovic, Sasa V. [2 ]
Cheng, Qifeng [1 ]
机构：
[1] Univ Oxford, Oxford OX1 3PJ, England
[2] Univ Magdeburg, Inst Automat Engn, D-39106 Magdeburg, Germany
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2011年 / 56卷 / 01期
关键词：
Constrained control; model predictive control (MPC); probabilistic constraints; stochastic systems; LINEAR-SYSTEMS; STATE; NMPC;
D O I：
10.1109/TAC.2010.2086553
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Stochastic model predictive control (MPC) strategies can provide guarantees of stability and constraint satisfaction, but their online computation can be formidable. This difficulty is avoided in the current technical note through the use of tubes of fixed cross section and variable scaling. A model describing the evolution of predicted tube scalings facilitates the computation of stochastic tubes; furthermore this procedure can be performed offline. The resulting MPC scheme has a low online computational load even for long prediction horizons, thus allowing for performance improvements. The efficacy of the approach is illustrated by numerical examples.
引用
收藏
页码：194 / 200
页数：8
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