Model Predictive Control for Collision Avoidance of Networked Vehicles Using Lagrangian Relaxation

被引：9

作者：

Alrifaee, Bassani ^{[1
]}

Kostyszyn, Kevin ^{[1
]}

Abel, Dirk ^{[1
]}

机构：

[1] Rhein Westfal TH Aachen, Dept Mech Engn, Inst Automat Control, D-52074 Aachen, Germany

来源：

IFAC PAPERSONLINE | 2016年 / 49卷 / 03期

关键词：

Model Predictive Control; Non-convex Optimization; Lagrangian Relaxation; Collision Avoidance; Networked Vehicles;

D O I：

10.1016/j.ifacol.2016.07.072

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 focuses on designing a steering intervene on system based on Model Predictive Control. The goal is to avoid collisions between networked vehicles. Since the resulting program is non-convex, it is converted to a convex Semi-Definite Program by using Lagrangian relaxation. Furthermore, the utilized prediction vehicle model is linearized in an exact way to avoid errors due to linear approximation. Initially, the whole probleiim is formulated. Finally; the performance of the controller is presented in Model-in-the-Loop simulations. (C) 2016, IFAC (International Federation of Automatic Control) hosting by Elsevier Ltd. All rights reserved.

引用

页码：430 / 435

页数：6

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