PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 6, 2026Drones0 citationsOpen Access

Gain-Adaptive Fault-Tolerant Control for High-Speed UAVs with Cascade Event-Triggered Mechanism

View Full Paper
HZHaoyu ZhaoMacau University of Science and TechnologyGZGuoqing ZhangDalian Maritime UniversityHXHeye XiaoNorthwestern Polytechnical University

Key Points

  • This research aims to develop a robust fault-tolerant control strategy for high-speed unmanned aerial vehicles (UAVs).
  • Implemented gain-adaptive mechanism for bias fault compensation.
  • Developed cascaded event-triggered mechanism for efficient control updates.
  • Utilized fuzzy logic system to approximate unknown nonlinear terms in UAV dynamics.
  • Demonstrated improved tracking accuracy for UAV formations.
  • Reduced communication load in control inputs and adaptation loops.

Abstract

This paper presents a robust adaptive fault-tolerant control (FTC) strategy for the path-following maneuvering of a high-speed unmanned aerial vehicle (UAV) formation system. The designation integrates an actuator gain-adaptive mechanism which is capable of compensating partial loss of effectiveness and bias faults, with a cascaded event-triggered mechanism (ETM) that regulates both control-command updates and adaptation loops. To handle strong coupling and modeling uncertainties in the UAV dynamics, unknown nonlinear terms are approximated using a fuzzy logic system (FLS), and dynamic surface control (DSC) is employed to avoid differential explosion. A boundary-regulated intermediate control term further enhances robustness against time-varying gains. The cascaded ETM reduces communication and computation by enforcing update thresholds on control inputs and parameter-update signals. Lyapunov analysis establishes semi-global uniform ultimate boundedness of all closed-loop signals. Comparative simulations indicate improved tracking accuracy and reduced channel load relative to representative baselines.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhao et al. (2026) studied this question.

synapsesocial.com/papers/69faa1eb04f884e66b532938https://doi.org/10.3390/drones10050341
Ask AI
Helpful
Bookmark
Share
View Full Paper