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Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs
作者姓名:Zhihao CAI  Longhong WANG  Jiang ZHAO  Kun WU  Yingxun WANG
作者单位:1. School of Automation Science and Electrical Engineering, Beihang University;2. Flying College, Beihang University
基金项目:supported in part by the National Natural Science Foundation of China(No.61803009);;Fundamental Research Funds for the Central Universities,China(No.YWF-19-BJ-J-205);;Aeronautical Science Foundation of China(No.20175851032);
摘    要:The paper proposes a Virtual Target Guidance(VTG)-based distributed Model Predictive Control(MPC) scheme for formation control of multiple Unmanned Aerial Vehicles(UAVs).First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem(FHOCP) can be solved by swarm intelligent optimization algorithm.Then, a VTG approach is developed and integrated into the distributed MPC scheme...

收稿时间:19 March 2019

Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs
Zhihao CAI,Longhong WANG,Jiang ZHAO,Kun WU,Yingxun WANG.Virtual target guidance-based distributed model predictive control for formation control of multiple UAVs[J].Chinese Journal of Aeronautics,2020,33(3):1037-1056.
Institution:1. School of Automation Science and Electrical Engineering, Beihang University, Beijing 100083, China;2. Flying College, Beihang University, Beijing 100083, China
Abstract:The paper proposes a Virtual Target Guidance (VTG)-based distributed Model Predictive Control (MPC) scheme for formation control of multiple Unmanned Aerial Vehicles (UAVs). First, a framework of distributed MPC scheme is designed in which each UAV only shares the information with its neighbors, and the obtained local Finite-Horizon Optimal Control Problem (FHOCP) can be solved by swarm intelligent optimization algorithm. Then, a VTG approach is developed and integrated into the distributed MPC scheme to achieve trajectory tracking and obstacle avoidance. Further, an event-triggered mechanism is proposed to reduce the computational burden for UAV formation control, which takes into consideration the predictive state errors as well as the convergence of cost function. Numerical simulations show that the proposed VTG-based distributed MPC scheme is more computationally efficient to achieve formation control of multiple UAVs in comparison with the traditional distributed MPC method.
Keywords:Distributed Model Predictive Control (MPC)  Event-triggered mechanism  Formation control  Obstacle avoidance  Unmanned Aerial Vehicles (UAVs)  Virtual Target Guidance (VTG)
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