Leader-Follower UAV formation flight control based on feature modelling

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS Systems Science & Control Engineering Pub Date : 2023-10-11 DOI:10.1080/21642583.2023.2268153
Yafei Chen, Tao Deng
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Abstract

To solve the problems of backstepping error and poor dynamic tracking approach rate in traditional PID neural network control in UAV formation flight control, a Leader-Follower UAV formation flight control method based on feature modelling is proposed,and the pose relationship model between virtual follower and pilot is established by trajectory tracking and pose dynamic fitting. The pose distribution of thefollower is analyzed in the ground coordinate system, and the parameter information of linear velocity and angular velocity control of UAV is obtained, and the backstepping sliding mode formation controller is formed. The variable structure PID neural network controller is used to design the flight control law of UAV formation, and the fast piecewise power approaching factor is introduced into the PID controller to eliminate the chattering of sliding mode control. The simulation results show that this method can ensure the rapidity of UAV formation flight control also show strong anti-jamming ability. Due to the fast piecewise power approach rate, the UAVs can complete the UAV formation reorganization under disturbance and buffeting in a short time, and the trajectory tracking error approaches zero, and it has good anti-buffeting ability.
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基于特征建模的Leader-Follower无人机编队飞行控制
针对传统PID神经网络控制在无人机编队飞行控制中存在退步误差和动态跟踪接近率差的问题,提出了一种基于特征建模的Leader-Follower无人机编队飞行控制方法,并通过轨迹跟踪和位姿动态拟合建立了虚拟follower与飞行员的位姿关系模型。分析了从动件在地面坐标系中的位姿分布,获得了无人机的线速度和角速度控制参数信息,形成了反步滑模编队控制器。采用变结构PID神经网络控制器设计无人机编队飞行控制律,并在PID控制器中引入快速分段功率逼近因子,消除滑模控制的抖振。仿真结果表明,该方法既能保证无人机编队飞行控制的快速性,又表现出较强的抗干扰能力。由于具有较快的分段功率进近速率,能在短时间内完成扰动和抖振下的编队重组,且轨迹跟踪误差接近于零,具有良好的抗抖振能力。
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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