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
收藏
页码：263 / +
页数：3
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