@inproceedings{55518d02bc314fb1abe5749f7dba60a4,
title = "Distributed MPC Based on Distributed Evolutionary Game for Leaderless Formation Control",
abstract = "This paper develops a distributed model predictive control (MPC) algorithm for leaderless formation control problem subject to a time-varying communication topology. The motivation for this algorithm is the challenge in addressing the coupled constraints, like communication range constraints and collision avoidance constraints under the distributed architecture. To approach this problem, we formulate the MPC optimal control problem as an evolutionary game problem for two populations with coupled constraints. A distributed evolutionary game is designed to obtain the solution, through finding the Nash equilibrium point. The properties of the proposed distributed evolutionary game are given, including the stability of the Nash equilibrium point and invariance of the set of possible strategic distributions, even though the communication topology is time varying. Finally, a numerical simulation is provided to verify the efficacy of the proposed distributed control algorithm.",
keywords = "Distributed MPC, Evolutionary game, Formation control",
author = "Xiaoting Zhou and Li Dai and Teng Huang and Da Huo and Yuanqing Xia",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 5th Chinese Conference on Swarm Intelligence and Cooperative Control, CCSICC 2021 ; Conference date: 19-01-2022 Through 22-01-2022",
year = "2023",
doi = "10.1007/978-981-19-3998-3_160",
language = "English",
isbn = "9789811939976",
series = "Lecture Notes in Electrical Engineering",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "1716--1727",
editor = "Zhang Ren and Yongzhao Hua and Mengyi Wang",
booktitle = "Proceedings of 2021 5th Chinese Conference on Swarm Intelligence and Cooperative Control",
address = "Germany",
}