Statistical learning for optimal control of hybrid systems

被引：0

作者：

Piovesan, Jorge ^{[1
]}

Abdallah, Chaouki ^{[1
]}

Egerstedt, Magnus ^{[2
]}

Tanner, Herbert ^{[3
]}

Wardi, Yorai ^{[2
]}

机构：

[1] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA

[2] Georgia Inst Technol, Sch Elect & Comp Engn, Atlanta, GA 30332 USA

[3] Univ New Mexico, Dept Mech Engn, Albuquerque, NM 87131 USA

来源：

2007 AMERICAN CONTROL CONFERENCE, VOLS 1-13 | 2007年

关键词：

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 explore a randomized alternative for the optimization of hybrid systems' performance. The basic approach is to generate samples from the family of possible solutions, and to test them on the plant's model to evaluate their performance. This result is obtained by first presenting the general hybrid optimal control problem, and then converting it into an optimization problem within a statistical learning framework. The results are applied to examples already existing in the literature, in order to highlight certain operational aspects of the proposed methods.

引用

页码：2053 / +

页数：2

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