Probabilistic Constrained MPC for Multiplicative and Additive Stochastic Uncertainty

被引：130

作者：

Cannon, Mark ^{[1
]}

Kouvaritakis, Basil ^{[1
]}

Wu, Xingjian ^{[1
]}

机构：

[1] Univ Oxford, Dept Engn Sci, Oxford OX1 3PJ, England

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2009年 / 54卷 / 07期

关键词：

Constrained control; fatigue life; optimization; stochastic control; MODEL-PREDICTIVE CONTROL;

D O I：

10.1109/TAC.2009.2017970

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The technical note develops a receding horizon control strategy to guarantee closed-loop convergence and feasibility in respect of soft constraints. Earlier results [1] addressed the case of multiplicative uncertainty only. The present technical note extends these to the more general case of additive and multiplicative uncertainty and proposes a method of handling probabilistic constraints. The results are illustrated by a simple design study considering the control of a wind turbine.

引用

页码：1626 / 1632

页数：7

共 13 条

[1]

Batina I, 2002, IEEE DECIS CONTR P, P1564, DOI 10.1109/CDC.2002.1184742

[2]

Boyd S., 1994, LINEAR MATRIX INEQUA

[3]

Burton T., 2001, Wind Energy Handbook

[4] Model predictive control for systems with stochastic multiplicative uncertainty and probabilistic constraints [J].