A Semidefinite Programming Method for Moment Approximation in Stochastic Differential Algebraic Systems

被引：0

作者：

Lamperski, Andrew ^{[1
]}

Dhople, Sairaj ^{[1
]}

机构：

[1] Univ Minnesota, Dept Elect & Comp Engn, Minneapolis, MN 55455 USA

来源：

2017 IEEE 56TH ANNUAL CONFERENCE ON DECISION AND CONTROL (CDC) | 2017年

基金：

美国国家科学基金会;

关键词：

DYNAMICS;

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 presents a continuous-time semidefinite-programming method for bounding statistics of stochastic processes governed by stochastic differential-algebraic equations with trigonometric and polynomial nonlinearities. Upper and lower bounds on the moments are then computed by solving linear optimal control problems for an auxiliary linear control system in which the states and inputs are systematically constructed vectors of mixed algebraic-trigonometric moments. Numerical simulations demonstrate how the method can be applied to solve moment-closure problems in representative systems described by stochastic differential algebraic equation models.

引用

页数：6

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