Unmanned Aerial Vehicles Formation Using Learning Based Model Predictive Control

被引：24

作者：

Hafez, Ahmed T. ^{[1
]}

Givigi, Sidney N. ^{[2
]}

Yousefi, Shahram ^{[3
]}

机构：

[1] Mil Tech Coll, Dept Elect Engn, Cairo, Egypt

[2] Royal Mil Coll Canada, Dept Elect & Comp Engn, Kingston, ON, Canada

[3] Queens Univ, Dept Elect & Comp Engn, Kingston, ON, Canada

来源：

ASIAN JOURNAL OF CONTROL | 2018年 / 20卷 / 03期

关键词：

Model predictive control; learning control; unmanned aerial vehicles; vehicle formation; optimization; cooperative robotics; SYSTEMS;

D O I：

10.1002/asjc.1774

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 solution for the formation flight problem for multiple unmanned aerial vehicles (UAVs) cooperating to execute a required mission. Learning Based Model Predictive Control (LBMPC) is implemented on the team of UAVs in order to accomplish the required formation. All flight simulations respect Reynold's rules of flocking to avoid UAV collisions with nearby flockmates, match the team members velocity and stay close to each other during the formation. The main contribution of this paper lies in the application of LBMPC to solve the problem of formation for an autonomous team of UAVs. The proposed solution is theoretically, by the application of analysis to the problem, demonstrated to be stable. Moreover, simulations support the findings of the paper. The main contributions of this paper are the proposed LBMPC formulation for formation of vehicles with uncertainty in their models, and the theoretical analysis of the solution.

引用

页码：1014 / 1026

页数：13

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