Neural Networks-based Robust Adaptive Dynamic Surface Sliding Mode Control of Flight Path Angle with Tracking Error Constraints

Sen Wang, Guoqiang Zhu, Xinkai Chen, Xiuyu Zhang, Junjie Xu, Xiaoming Li, Hong Cao

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an adaptive neural network based dynamic surface sliding-mode control (ANDSSMC) scheme is proposed for the aircraft flight path angle system with external disturbances and parameters uncertainties. By using the minimum learning technology, only one parameter needs to be updated online at each design step, so that the controller is much simpler and the computational burden can be greatly reduced. The tracking error constraint functions are introduced to ensure the tracking error keep in the prescribed boundaries, and the tracking performance is improved. By combing dynamic surface controller design technique with sliding mode method, the proposed controller can not only eliminate the problem of "explosion of complexity" existing in traditional backstepping approach but also improve the robustness of the system. By using the Lyapunov theory, it is proved that all signals of the closed- loop system are uniformly ultimately bounded and the tracking performance has been achieved. Finally, the simulation results are carried out to validate the effectiveness of the proposed control algorithm.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages587-592
Number of pages6
ISBN (Electronic)9781728136660
DOIs
Publication statusPublished - 2019 Jun 1
Event28th IEEE International Symposium on Industrial Electronics, ISIE 2019 - Vancouver, Canada
Duration: 2019 Jun 122019 Jun 14

Publication series

NameIEEE International Symposium on Industrial Electronics
Volume2019-June

Conference

Conference28th IEEE International Symposium on Industrial Electronics, ISIE 2019
CountryCanada
CityVancouver
Period19/6/1219/6/14

Fingerprint

Flight paths
Sliding mode control
Neural networks
Controllers
Backstepping
Closed loop systems
Explosions
Aircraft

Keywords

  • Dynamic surface control
  • Flight path angle
  • Performance function
  • Sliding mode control

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Control and Systems Engineering

Cite this

Wang, S., Zhu, G., Chen, X., Zhang, X., Xu, J., Li, X., & Cao, H. (2019). Neural Networks-based Robust Adaptive Dynamic Surface Sliding Mode Control of Flight Path Angle with Tracking Error Constraints. In Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019 (pp. 587-592). [8781536] (IEEE International Symposium on Industrial Electronics; Vol. 2019-June). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ISIE.2019.8781536

Neural Networks-based Robust Adaptive Dynamic Surface Sliding Mode Control of Flight Path Angle with Tracking Error Constraints. / Wang, Sen; Zhu, Guoqiang; Chen, Xinkai; Zhang, Xiuyu; Xu, Junjie; Li, Xiaoming; Cao, Hong.

Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 587-592 8781536 (IEEE International Symposium on Industrial Electronics; Vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, S, Zhu, G, Chen, X, Zhang, X, Xu, J, Li, X & Cao, H 2019, Neural Networks-based Robust Adaptive Dynamic Surface Sliding Mode Control of Flight Path Angle with Tracking Error Constraints. in Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019., 8781536, IEEE International Symposium on Industrial Electronics, vol. 2019-June, Institute of Electrical and Electronics Engineers Inc., pp. 587-592, 28th IEEE International Symposium on Industrial Electronics, ISIE 2019, Vancouver, Canada, 19/6/12. https://doi.org/10.1109/ISIE.2019.8781536
Wang S, Zhu G, Chen X, Zhang X, Xu J, Li X et al. Neural Networks-based Robust Adaptive Dynamic Surface Sliding Mode Control of Flight Path Angle with Tracking Error Constraints. In Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 587-592. 8781536. (IEEE International Symposium on Industrial Electronics). https://doi.org/10.1109/ISIE.2019.8781536
Wang, Sen ; Zhu, Guoqiang ; Chen, Xinkai ; Zhang, Xiuyu ; Xu, Junjie ; Li, Xiaoming ; Cao, Hong. / Neural Networks-based Robust Adaptive Dynamic Surface Sliding Mode Control of Flight Path Angle with Tracking Error Constraints. Proceedings - 2019 IEEE 28th International Symposium on Industrial Electronics, ISIE 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 587-592 (IEEE International Symposium on Industrial Electronics).
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abstract = "In this paper, an adaptive neural network based dynamic surface sliding-mode control (ANDSSMC) scheme is proposed for the aircraft flight path angle system with external disturbances and parameters uncertainties. By using the minimum learning technology, only one parameter needs to be updated online at each design step, so that the controller is much simpler and the computational burden can be greatly reduced. The tracking error constraint functions are introduced to ensure the tracking error keep in the prescribed boundaries, and the tracking performance is improved. By combing dynamic surface controller design technique with sliding mode method, the proposed controller can not only eliminate the problem of {"}explosion of complexity{"} existing in traditional backstepping approach but also improve the robustness of the system. By using the Lyapunov theory, it is proved that all signals of the closed- loop system are uniformly ultimately bounded and the tracking performance has been achieved. Finally, the simulation results are carried out to validate the effectiveness of the proposed control algorithm.",
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