Suboptimal Search Strategies With Bounded Computational Complexity to Solve Long-Horizon Direct Model Predictive Control Problems

被引：0

作者：

Karamanakos, Petros ^{[1
]}

Geyer, Tobias ^{[2
]}

Kennel, Ralph ^{[1
]}

机构：

[1] Tech Univ Munich, Inst Elect Drive Syst & Power Elect, D-80333 Munich, Germany

[2] ABB Corp Res, CH-5405 Baden, Switzerland

来源：

2015 IEEE ENERGY CONVERSION CONGRESS AND EXPOSITION (ECCE) | 2015年

关键词：

LATTICE;

D O I：

暂无

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Search algorithms that reduce the time to solve the direct model predictive control (MPC) problem are proposed in this paper. By allowing for suboptimal solutions, the computational complexity of the underlying optimization problem can be significantly reduced, albeit by sacrificing (to a certain degree) optimality. Two approaches are presented and discussed. The first approach requires quadratic time, making it a very efficient candidate for solving the examined problem. Thanks to the second approach, a preset upper limit on the operations performed in real time is not exceeded, thus guaranteeing real-time termination in all runs. To highlight the effectiveness of the introduced strategies, a variable speed drive system with a three-level voltage source inverter is used as an illustrative example.

引用

页码：334 / 341

页数：8