Sequential Monte Carlo for Model Predictive Control

被引：0

作者：

Kantas, N. ^{[1
]}

Maciejowski, J. M. ^{[1
]}

Lecchini-Visintini, A. ^{[2
]}

机构：

[1] Univ Cambridge, Dept Engn, Cambridge CB2 1PZ, England

[2] Univ Leicester, Dept Engn, Leicester LE1 7RH, Leics, England

来源：

NONLINEAR MODEL PREDICTIVE CONTROL: TOWARDS NEW CHALLENGING APPLICATIONS | 2009年 / 384卷

基金：

英国工程与自然科学研究理事会;

关键词：

Stochastic optimisation; Stochastic MPC; Sequential Monte Carlo; PARTICLE METHODS;

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：

This paper proposes the use of Sequential Monte Carlo (SMC) as the computational engine for general (non-convex) stochastic Model Predictive Control (MPC) problems. It shows how SMC methods can be used to find global optimisers of non-convex problems, in particular for solving open-loop stochastic control problems that arise at the core of the usual receding-horizon implementation of MPC. This allows the MPC methodology to be extended to nonlinear non-Gaussian problems. We illustrate the effectiveness of the approach by means of numerical examples related to coordination of moving agents.

引用

页码：263 / +

页数：3

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