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Dynamic event-triggered adaptive neural predefined-time attitude control for a QUAV
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DOI：10.1007/s11071-026-12944-4.png)
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Abstract
En 中文
This paper addresses the predefined-time attitude tracking control problem for quadrotor UAV (QUAV) attitude systems subject to model uncertainties, external disturbances and actuator faults, and proposes a dynamic event-triggered predefined-time adaptive sliding mode control strategy. First, radial basis function neural networks (RBFNN) are adopted to approximate the unknown system terms coupled by model uncertainties and actuator faults. Meanwhile, a continuous predefined-time disturbance observer is developed to estimate and dynamically compensate for the composite residual unknown terms consisting of neural network approximation errors and external disturbances. Furthermore, an adaptive update law is designed to realize online tuning of neural network weights, and an event-triggered mechanism with dynamic auxiliary variables is introduced to reduce the computational and communication overhead caused by frequent controller updates. A continuous piecewise nonsingular sliding surface is also constructed to avoid the singularity inherent in conventional predefined-time sliding mode control. Lyapunov stability analysis establishes the practical predefined-time stability of the closed-loop system. Comparative simulation results demonstrate that under the considered operating conditions with coexisting composite uncertainties and actuator faults, the proposed scheme achieves favorable attitude tracking accuracy and fault-tolerant control performance. Moreover, it reduces the frequency of control updates, extends inter-event intervals, and substantially saves the communication and computational resources of the system.
Keywords:
Sliding mode control
Predefined-time control
Dynamic event-triggered strategy
Quadrotor unmanned aerial vehicle
Fault-tolerant attitude tracking control
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6
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1.4W
Citations:
4.1W
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