Nonlinear Uncertainty Control with Iterative Covariance Steering

被引：0
|
作者：
Ridderhof, Jack [1 ]
Okamoto, Kazuhide [1 ]
Tsiotras, Panagiotis [2 ,3 ]
机构：
[1] Georgia Inst Technol, Sch Aerosp Engn, Atlanta, GA 30332 USA
[2] Georgia Inst Technol, D Guggenheim Sch Aerosp Engn, Atlanta, GA 30332 USA
[3] Georgia Inst Technol, Inst Robot & Intelligent Machines, Atlanta, GA 30332 USA
来源：
2019 IEEE 58TH CONFERENCE ON DECISION AND CONTROL (CDC) | 2019年
关键词：
RISK ALLOCATION;
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 considers the problem of steering the state distribution of a nonlinear stochastic system from an initial Gaussian to a terminal distribution with a specified mean and covariance, subject to probabilistic path constraints. An algorithm is developed to solve this problem by iteratively solving an approximate linearized problem as a convex program. This method, which we call iterative covariance steering (iCS), is numerically demonstrated by controlling a double integrator with quadratic drag force subject to additive Brownian noise while satisfying probabilistic path constraints.
引用
收藏
页码：3484 / 3490
页数：7
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