Skip to main content
Log in

Research on UAV flight control and communication method based on fuzzy adaptive

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

In order to improve the intelligent perception and adaptability of the 6G network, drones joined this challenge. For large-scale long-range Unmanned Aerial Vehicle (UAV), most of the time during normal flight belongs to fixed altitude flight. It is required to sail along the planned optimal path. Whether it can fly along the optimal path is mainly attributed to the tracking problem of horizontal flight trajectory. In order to minimize the UAV horizontal plane tracking error, it is necessary to consider the influence of many factors (such as strong winds, heavy rain, obstacles, etc.). Due to the complexity of High-Altitude environment, these disturbances are uncertain. In addition, there are some dynamic errors in the model of UAV control system, and these errors also have uncertainties. And, due to the change of global planning path coordinates, the control system needs to adjust the set value in real time during AUV horizontal trajectory tracking, and the conventional control algorithm is difficult to meet the requirements. Therefore, firstly, the influence of prediction uncertainty of grey prediction on AUV horizontal track tracking control is used; Then the grey prediction is improved according to the practical application; Ultimately, the control law is designed by combining the grey prediction with the control method. Finally, the grey prediction fuzzy adaptive PID method of UAV flight control is applied to the planned path simulation, and good control results are obtained.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

Data availability

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

References

  1. He Zhenqi; Yao Lu.(2021) .Research on an Obstacle Avoidance Method for UAV. Mathematical Problems in Engineering. 2021.

  2. Jian, D., & Bin, He. (2018). Novel fuzzy PID-type iterative learning control for quadrotor UAV. SENSORS, 19(1), 24.

    Article  MathSciNet  Google Scholar 

  3. Wai, R.-J., & Prasetia, A. S. (2019). Adaptive neural network control and optimal path planning of UAV surveillance system with energy consumption prediction. IEEE ACCESS., 7, 126137–126153.

    Article  Google Scholar 

  4. Lindqvist, B., Mansouri, S. S., Agha-Mohammadi, A.-A., & Nikolakopoulos, G. (2020). Nonlinear MPC for collision avoidance and control of UAVs with dynamic obstacles. IEEE Robotics and Automation Letters, 5(4), 6001–6008.

    Article  Google Scholar 

  5. Mendoza, A.-R., & Yu, W. (2023). Fuzzy adaptive control law for trajectory tracking based on a fuzzy adaptive neural PID controller of a multi-rotor unmanned aerial vehicle. International Journal of Control, Automation and Systems, 21(2), 658–670.

    Article  Google Scholar 

  6. Hu, Y.-C. (2020). A multivariate grey prediction model with grey relational analysis for bankruptcy prediction problems. Soft Computing, 24(6), 4259–4268.

    Article  Google Scholar 

  7. Wang, Y., Shi, Y., Cai, M., Xu, W., & Yu, Q. (2018). Optimization of air-fuel ratio control of fuel-powered UAV engine using adaptive fuzzy-PID. Journal of the Franklin Institute-Engineering and Applied Mathematics, 355(17), 8554–8575.

    Article  MathSciNet  MATH  Google Scholar 

  8. Basri, M. A. M., Husain, A. R., & Danapalasingam, K. A. (2015). A hybrid optimal backstepping and adaptive fuzzy control for autonomous quadrotor helicopter with time-varying disturbance. Proceedings of the Institution of Mechanical Engineers, 229, 2178–2195.

    Article  Google Scholar 

  9. Ermeydan, A., & Kiyak, E. (2017). Fault tolerant control against actuator faults based on enhanced PID controller for a quadrotor. Aircraft Engineering and Aerospace Technology, 89(3), 468–476.

    Article  Google Scholar 

  10. Tong, W., Zhao, T., Duan, Q., Zhang, H., & Mao, Y. (2022). Non-singleton interval type-2 fuzzy PID control for high precision electro-optical tracking system. ISA Transactions, 120, 258–270.

    Article  Google Scholar 

  11. Sun, C., Liu, M., Liu, C., Feng, X., & Wu, H. (2021). An industrial quadrotor UAV control method based on fuzzy adaptive linear active disturbance rejection control. Electronics, 10(4), 376–398.

    Article  Google Scholar 

Download references

Acknowledgements

UAV intelligent control technology innovation team (Grant: KJTD20-002), School level scien-tific research projects(Grant: 21XHTD-03), Project of Shaanxi Vocational and technical education society in 2022 (Grant: 2022SZX096)

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhenqi He.

Ethics declarations

Conflict of interest

The author(s) declared no potential conflicts of interest with respect to the research, author-ship, and/or publication of this article.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

He, Z. Research on UAV flight control and communication method based on fuzzy adaptive. Wireless Netw (2023). https://doi.org/10.1007/s11276-023-03408-3

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-023-03408-3

Keywords

Navigation