Moment Based Model Predictive Control for Systems with Additive Uncertainty

被引：0

作者：

Saltik, M. Bahadir ^{[1
]}

Ozkan, Leyla ^{[1
]}

Weiland, Siep ^{[1
]}

Ludlage, Jobert H. A. ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Control Syst Grp, Eindhoven, Netherlands

来源：

2017 AMERICAN CONTROL CONFERENCE (ACC) | 2017年

关键词：

STABILITY;

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, we present a model predictive control (MPC) strategy based on the moments of the state variables and the cost functional. The statistical properties of the state predictions are calculated through the open loop iteration of dynamics and used in the formulation of MPC cost function. We show that the moment based formulation yields predictive control problems which are computationally simpler to solve compared to the existing robust MPC formulations, while providing statistical robustness properties. We apply the proposed MPC technique to a simple simulation example to demonstrate its effectiveness.

引用

页码：3072 / 3077

页数：6

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