Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (3): 871-882.doi: 10.13229/j.cnki.jdxbgxb20221034

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Time⁃varying formation control of multi⁃quadrotor unmanned aerial vehicles based on state observer

Ya-jing YU1(),Jian GUO1,Rong-hao WANG2,Wei QIN3,4,Ming-wu SONG4,Zheng-rong XIANG1()   

  1. 1.School of Automation，Nanjing University of Science and Technology，Nanjing 210094，China
    2.Academy of National Defense Engineering，Army Engineering University，Nanjing 210007，China
    3.Defense Innovation Institute，Academy of Military Sciences，Beijing 100091，China
    4.Tianjin Artificial Intelligence Innovation Center，Tianjin 300450，China
  • Received:2022-08-15 Online:2023-03-01 Published:2023-03-29
  • Contact: Zheng-rong XIANG E-mail:yajing.yu@njust.edu.cn;xiangzr@njust.edu.cn

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Abstract:

A distributed adaptive sliding mode time-varying formation control scheme based on neural network state observer was proposed to solve the problem of unmeasurable information of multi-quadrotor unmanned aerial vehicles （UAVs） system in directed switching communication network. Firstly， by designing a distributed time-varying formation control protocol， UAVS can communicate with each other only by the status information of their neighbors， which is free from the dependence on global information. Due to the underdrive characteristic， a position-assisted controller was designed to solve the target information of two attitude angles and control thrust. At the same time， a neural network state observer was designed to observe the unmeasurable information of the system， and the observed values were feedback to the adaptive sliding mode controller in real time， which improves the robustness of the UAV system. The quadrotor UAV formation system is analyzed by Lyapunov's theorem， and it proved that the multi-UAV formation error is bounded and converges to near zero. The simulation results show that the proposed control method can realize the time-varying formation control of the multi-UAV system and verify the validity of the theoretical results.

Key words: unmanned aerial vehicles system, directed switching communications network, neural network state observer, time-varying formation control, adaptive sliding mode control

CLC Number: 

  • V249.121

Fig.1

Structure diagram of quadrotor UAV3"

Fig.2

Structure of double loop control system"

Fig.3

Possible communication network structures"

Table 1

Initial states of UAVs"

名称位置/m速度/（m·s-1）姿态角/（°）
UAV1（-50，50，0）（1.5，1.5，1.5）（0，0，0）
UAV2（-55，75，0）（1.2，1.2，1.2）（0，0，0）
UAV3（75，30，0）（1.6，1.6，1.6）（0，0，0）
UAV4（150，10，0）（1.2，1.2，1.2）（0，0，0）

Fig.4

3D track diagram of UAVs time-varying formation"

Fig.5

Control input"

Fig.6

Adaptive law"

Fig.7

Position，velocity and attitude angle curves of four UAVs and their observation errors"

Fig.8

Tracking errors of four UAVs"

Fig.9

Observational error curves of position， velocity and attitude of UAV 2 withtwo different algorithms"

Fig.10

Tracking error curves of UAV2 with two different algorithms"

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