@inproceedings{3a0b9fcd2dd740ad826a0a590d120f39,
title = "Optimal adaptive tracking consensus for multi-vehicle systems with periodic sampling",
abstract = "This paper proposes a robust model predictive control approach to address the optimized adaptive tracking consensus problem for multi-vehicle systems with periodic sampling under the directed communication topology. Unlike the existing works using the global information to achieve the tracking consensus, the proposed optimized tracking cooperative control strategy considers that the control gain is not fixed and the states information exchange be affected by the recourse constraints. By introducing the conditions of the optimized consensus and the communication cost, the adaptive tracking cooperative control law with bounded parameters is developed based on the periodic samples. It shows that the multi-vehicle systems will reach the optimized consensus, if the proposed sampling condition is satisfied. Simulation results are provided to verify the effectiveness of the proposed approach.",
keywords = "Multi-vehicle systems, adaptive consensus, periodic sampling, robust model predictive control, tracking consensus",
author = "Bohui Wang and Weisheng Chen and Hao Dai and Xinpeng Fang and Jingcheng Wang and Bin Zhang and Zhengqiang Zhang and Xingguo Qiu",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 Chinese Automation Congress, CAC 2017 ; Conference date: 20-10-2017 Through 22-10-2017",
year = "2017",
month = dec,
day = "29",
doi = "10.1109/CAC.2017.8243461",
language = "英语",
series = "Proceedings - 2017 Chinese Automation Congress, CAC 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3901--3906",
booktitle = "Proceedings - 2017 Chinese Automation Congress, CAC 2017",
}