MODEL PREDICTIVE CONTROL OF CONSTRAINED WITH NON LINEAR STOCHASTIC PARAMETERS SYSTEMS

被引：0

作者：

Dombrovskii, V. V. ^{[1
,2
,3
]}

Obyedko, T. U. ^{[4
]}

机构：

[1] Tomsk State Univ, Econ Dept, Tomsk, Russia

[2] Tomsk State Univ, Econ Dept, Tech Sci, Tomsk, Russia

[3] Tomsk State Univ, Econ Dept, Dept Math Methods & Informat Technol Econ, Tomsk, Russia

[4] Tomsk State Univ, Fac Appl Math & Cybernet, Tomsk, Russia

来源：

VESTNIK TOMSKOGO GOSUDARSTVENNOGO UNIVERSITETA-UPRAVLENIE VYCHISLITELNAJA TEHNIKA I INFORMATIKA-TOMSK STATE UNIVERSITY JOURNAL OF CONTROL AND COMPUTER SCIENCE | 2011年 / 16卷 / 03期

关键词：

discrete time control systems; model predictive control (MPC); non-linear stochastic parameters; multiplicative noise; constraints; computational methods; stochastic control;

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 consider the model predictive control problem of discrete-time systems with non-linear random depended parameters for which only the first and second conditional distribution moments, the conditional autocorrelations and the mutual cross-correlations are known. The open-loop feedback control strategy is derived subject to hard constraints on the control variables. The approach is advantageous because the rich arsenal of methods of non-linear estimation or the results of nonparametric estimation may be used directly for describing characteristics of random parameter sequences.

引用

页码：5 / 12

页数：8

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