Safety-Critical Control and Path Following by Formations of Agents with Control Barrier Functions using Distributed Model Predictive Control

被引：1
|
作者：
Sun, YiZhi [1 ]
Wu, Di [1 ]
Gao, Liang [1 ]
Gao, YongFeng [1 ]
Pan, Yan [2 ]
Ding, Nan [3 ]
机构：
[1] Dalian Univ Technol, Sch Control Sci & Control Engn, Dalian 116024, Peoples R China
[2] Dalian Univ Technol, Sch Naval Arch & Ocean Engn, Dalian 116024, Peoples R China
[3] Dalian Univ Technol, Sch Comp Sci & technol, Dalian 116024, Peoples R China
来源：
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
基金：
中国国家自然科学基金;
关键词：
formation control; collision avoidance; nonlinear model predictive control; discrete-time control barrier function;
D O I：
10.1109/CCDC58219.2023.10326991
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 propose a safety-critical formation control method based on distributed nonlinear model predictive control strategy, which controls the path following and formation maintenance of the multiple mobile robots, while ensuring the collision avoidance. Firstly, we adopt the distributed framework with high real time performance. Secondly, based on the distributed optimization framework, discrete-time control barrier function constraints are transformed into smooth differentiable constraints to complete the polytopic obstacle avoidance with a small horizon by using the strong duality of convex optimization. Finally, the simulation results of three robots are given to prove the effectiveness of the proposed algorithm, and it can realize the local path generation based on real-time optimization in the narrow environment.
引用
收藏
页码：1818 / 1823
页数：6
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