Skip to main content
SpringerLink
Log in

A Novel Trajectory Tracking Controller for UAV with Uncertainty Based on RBF and Prescribed Performance Function

  • Conference paper
  • First Online:
Neural Computing for Advanced Applications (NCAA 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1637))

Included in the following conference series:

  • 679 Accesses

Abstract

This paper proposes a novel trajectory tracking controller based on RBF neural network and fractional-order sliding mode control (FO-SMC). First, the prescribed performance control (PPC) is introduced into the system to make the tracking error converge to the predefined set. Then, the fractional-order calculus is introduced into SMC to alleviate the chattering of the system. Considering that RBF neural network can compensate for the uncertainty of the UAV motion model, RBF is introduced into the design of the controller. Besides, the Lyapunov theorem proves the stability of the system, and all signals in the closed-loop system are stable. Finally, a case study is carried out through simulation.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Huang H., Savkin A. V., Huang C.: Decentralized autonomous navigation of a uav network for road traffic monitoring. IEEE Trans. Aerosp. Electron. Syst. 57(4), 2558–2564 (2021). https://doi.org/10.1109/TAES.2021.3053115

  2. Savkin A. V., Huang H.: Range-based reactive deployment of autonomous drones for optimal coverage in disaster areas. IEEE Trans. Syst. Man Cybern. Syst. 51(7), 4606–4610 (2021). https://doi.org/10.1109/TSMC.2019.2944010

  3. Huang H., Savkin A. V.: Deployment of charging stations for drone delivery assisted by public transportation vehicles. IEEE Trans. Intell. Transp. Syst. (2021). https://doi.org/10.1109/TITS.2021.3136218

  4. Xu L. X., Ma H. J., Guo D., Xie A. H., Song D. L.: Backstepping sliding-mode and cascade active disturbance rejection control for a quadrotor UAV. IEEE/ASME Trans. Mechatron. 25(6), 2743–2753 (2020). https://doi.org/10.1109/TMECH.2020.2990582

  5. Hu X., Liu J.: Research on UAV balance control based on expert-fuzzy adaptive PID. In: 2020 IEEE International Conference on Advances in Electrical Engineering and Computer Applications (AEECA), pp. 787–789 (2020). https://doi.org/10.1109/AEECA49918.2020.9213511

  6. Li C., Luo Z., Xu D., Wu W., Sheng Z.: Online trajectory optimization for UAV in uncertain environment. In: 2020 39th Chinese Control Conference (CCC), pp. 6996–7001 (2020). https://doi.org/10.23919/CCC50068.2020.9189144

  7. Jiao R., Chou W., Rong Y.: Disturbance observer-based backstepping control for quadrotor UAV manipulator attitude system. In: 2020 Chinese Automation Congress (CAC), pp. 2523–2526 (2020). https://doi.org/10.1109/CAC51589.2020.9327203

  8. Xu M., Shi W.: RBF neural network PID trajectory tracking based on 6-PSS parallel robot. In: 2019 Chinese Automation Congress (CAC), pp. 5674–5678 (2019). https://doi.org/10.1109/CAC48633.2019.8996255

  9. Li, X., Zhu, J.: Weighted average consensus in directed networks of multi-agents with time-varying delay. In: Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.) NCAA 2021. CCIS, vol. 1449, pp. 71–82. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-5188-5_6

    Chapter  Google Scholar 

  10. Zhang, H., Yang, Z., Zhang, Z., Wu, Z., Hao, T. (eds.): NCAA 2021. CCIS, vol. 1449. Springer, Singapore (2021). https://doi.org/10.1007/978-981-16-5188-5

    Book  Google Scholar 

  11. Jose, A.F., Marcelo, T., Gaardo, G.A.: Evolutionary reactive behavior for mobile robots navigation. Cybern. Intell. Syst. 1, 532–537 (2004)

    Google Scholar 

  12. Nelson, A.L., Grant, E., Gatwtti, J.M.: Maze exploration behaviors using an integrated evolutionary robotic environment. Robot. Auton. Syst. 46, 159–173 (2004)

    Article  Google Scholar 

  13. Nelson, A.L., Grant, E., Barlow, G.: Evolution of complex autonomous robot behaviors using competitive fitness. Integr. Knowl. Intensive Multi-Agent Syst. 1, 145–150 (2003)

    Article  Google Scholar 

  14. Pang, K.K., Prahlad, V.: Evolution of control systems for mobile robots. Evol. Comput. 1, 617–622 (2002)

    Google Scholar 

  15. Qiu J., Wang T., Sun K., Rudas I.J., Gao H: Disturbance observer-based adaptive fuzzy control for strict-feedback nonlinear systems with finite-time prescribed performance. IEEE Trans. Fuzzy Syst. PP(99), 1 (2021)

    Google Scholar 

  16. Sui, S., Chen, C.L.P., Tong, S.: A novel adaptive NN prescribed performance control for stochastic nonlinear systems. IEEE Trans. Neural Netw. Learn. Syst. 32(7), 3196–3205 (2021)

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgement

This work was supported in part by the funds of the National Natural Science Foundation of China under Grant 61873306.

Author information

Authors and Affiliations

  1. College of Information Science and Engineering, Northeastern University, Shenyang, 110819, People’s Republic of China

    Xuelei Qi & Chen Li

  2. School of Automation Science and Engineering, South China University of Technology, Guangzhou, 510641, People’s Republic of China

    Hongjun Ma

  3. Key Laboratory of Autonomous Systems and Networked Control, Ministry of Education, Unmanned Aerial Vehicle Systems Engineering Technology Research Center of Guangdong, Guangzhou, People’s Republic of China

    Hongjun Ma

Authors
  1. Xuelei Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chen Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongjun Ma
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hongjun Ma .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Shenzhen, China

    Haijun Zhang

  2. University of Jinan, Jinan, China

    Yuehui Chen

  3. Shenzhen University, Shenzhen, China

    Xianghua Chu

  4. Hefei University of Technology, Hefei, China

    Zhao Zhang

  5. South China Normal University, Guangzhou, China

    Tianyong Hao

  6. Chongqing University, Chongqing, China

    Zhou Wu

  7. Western University, London, ON, Canada

    Yimin Yang

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Qi, X., Li, C., Ma, H. (2022). A Novel Trajectory Tracking Controller for UAV with Uncertainty Based on RBF and Prescribed Performance Function. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1637. Springer, Singapore. https://doi.org/10.1007/978-981-19-6142-7_26

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-6142-7_26

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-6141-0

  • Online ISBN: 978-981-19-6142-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation