期刊
CHINESE JOURNAL OF AERONAUTICS
卷 33, 期 11, 页码 2877-2897出版社
ELSEVIER SCIENCE INC
DOI: 10.1016/j.cja.2020.04.028
关键词
Chaotic Grey Wolf Optimization (CGWO); Coordination control; Distributed Model Predictive Control (MPC); Event-triggered strategy; Multi-UAV
资金
- National Natural Science Foundation of China [61803009, 61903084]
- Fundamental Research Funds for the Central Universities of China [YWF-20-BJ-J-542]
- Aeronautical Science Foundation of China [20175851032]
The paper proposes a new swarm intelligence-based distributed Model Predictive Control (MPC) approach for coordination control of multiple Unmanned Aerial Vehicles (UAVs). First, a distributed MPC framework is designed and each member only shares the information with neighbors. The Chaotic Grey Wolf Optimization (CGWO) method is developed on the basis of chaotic initialization and chaotic search to solve the local Finite Horizon Optimal Control Problem (FHOCP). Then, the distributed cost function is designed and integrated into each FHOCP to achieve multi-UAV formation control and trajectory tracking with no-fly zone constraint. Further, an event-triggered strategy is proposed to reduce the computational burden for the distributed MPC approach, which considers the predicted state errors and the convergence of cost function. Simulation results show that the CGWO-based distributed MPC approach is more computationally efficient to achieve multi-UAV coordination control than traditional method. (c) 2020 Chinese Society of Aeronautics and Astronautics. Production and hosting by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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