Distributed Dynamic Optimization Scheme based on Cooperative Differential Games

被引：0

作者：

Zhu, Qiang ^{[1
]}

Shao, Zhijiang ^{[1
]}

Biegler, Lorenz T. ^{[2
]}

机构：

[1] Zhejiang Univ, Control Sci & Engn, Hangzhou, Peoples R China

[2] Carnegie Mellon Univ, Dept Chem Engn, Pittsburgh, PA 15213 USA

来源：

2019 CHINESE AUTOMATION CONGRESS (CAC2019) | 2019年

基金：

中国国家自然科学基金;

关键词：

distributed optimization; differential game; receding horizon optimization; system stability; MODEL-PREDICTIVE CONTROL;

D O I：

10.1109/cac48633.2019.8996972

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 study, a distributed dynamic optimization scheme called receding cooperative game optimization (RCGO) and the decomposition iterative direct method are proposed for numerically solving the complex distributed dynamic optimization based on cooperative differential games. The system stability under the RCGO is analyzed. Then, the RCGO and direct numerical method are utilized to address the distributed dynamic optimization problem for a complex chemical process network consisting of two continuous stirred tank reactors and a nonadiabatic flash. Economic, closed-loop and real-time performance indices of the RCGO are evaluated and compared with those of the receding centralized optimization (RCO) scheme. We conclude that the RCGO can effectively solve distributed dynamic optimization problems, the system stability under the RCGO can be guaranteed, and the RCGO has more advantages in overall performance indices for solving large-scale optimization problems than the RCO scheme.

引用

页码：1933 / 1938

页数：6

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