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Adaptive Neural Network-Based Fixed-Time Sliding Mode Control for Uncertain UAVs

  • Zhuang Liu
  • , Junyao Zang
  • , Jianing Tang*
  • , Ouyang Zhang
  • , Yabin Gao
  • , Jianxing Liu*
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Yunnan Key Laboratory of Unmanned Autonomous Systems
  • Yunnan Minzu University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper investigates the fixed time trajectory tracking control problem for an uncertain quadrotor unmanned aerial vehicle (UAV) system, enabling rapid, high precision following of a desired trajectory despite uncertainties and disturbances. A piecewise fixed time sliding mode variable is formulated to eliminate singularities associated with terminal sliding modes. An adaptive neural network (ANN) is developed to estimate system uncertainties and external disturbances within a predetermined fixed time, and an ANN based rapid fixed time controller is designed to ensure high accuracy trajectory tracking under these estimated perturbations. Rigorous Lyapunov analysis confirms the fast fixed time stability of the overall closed loop system. Comparative simulation studies validate the effectiveness of the proposed method.

Original languageEnglish
Title of host publicationProceedings - 2025 China Automation Congress, CAC 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5617-5622
Number of pages6
ISBN (Electronic)9798331589677
DOIs
StatePublished - 2025
Event2025 China Automation Congress, CAC 2025 - Harbin, China
Duration: 26 Sep 202528 Sep 2025

Publication series

NameProceedings - 2025 China Automation Congress, CAC 2025

Conference

Conference2025 China Automation Congress, CAC 2025
Country/TerritoryChina
CityHarbin
Period26/09/2528/09/25

Keywords

  • Adaptive neural network
  • UAVs
  • fixed-time control

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