Sampling variance update method in Monte Carlo Model Predictive Control

被引：0
|
作者：
Nakatani, Shintaro [1 ]
Date, Hisashi [2 ]
机构：
[1] Univ Tsukuba, Grad Sch Syst & Informat Engn, Ibaraki, Japan
[2] Univ Tsukuba, Fac Engn Informat & Syst, Ibaraki, Japan
来源：
IFAC PAPERSONLINE | 2020年 / 53卷 / 02期
关键词：
Optimal control theory; Monte-Carlo methods; Randomized methods; Model predictive and optimization-based control;
D O I：
10.1016/j.ifacol.2020.12.1855
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 study describes the influence of user parameters on control performance in a Monte-Carlo model predictive control (MCMPC). MCMPC based on Monte-Carlo sampling depends significantly on the characteristics of sampling distribution. We quantified the effect of user determinable parameters on control performance uisng the relatonship between the algorithm of MCMPC and convergence to the optimal solution. In particular, we investigated the limitations associated with the variance of sampling distribution causing a trade-off relationship with the convergence speed and accuracy of estimation. To overcome this limitation, we proposed two variance updating methods and new MCMPC algorithm. Furthermore, the effectiveness of the numeriacl simulation was verified. Copyright (C) 2020 The Authors.
引用
收藏
页码：1274 / 1281
页数：8
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