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Adaptive Fuzzy Model-Driven Dynamic Event-Triggered Quantization Control for QUAVs Using Command Filtered Backstepping
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AI
DOI：10.1109/TAES.2025.3629579.png)
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Abstract
En 中文
An adaptive fuzzy model-driven position and attitude tracking control algorithm for quad-rotor uncrewed aerial vehicles (QUAVs) is proposed in this article. The dynamic event-triggered mechanism and uniform quantizer are jointly designed to reduce the communication cost of feedback state signals, meeting the practical requirements of digital control systems. In addition, as an improvement over traditional zero-order-hold policy in event-triggered control, the adaptive fuzzy model is constructed to dynamically generate estimated state values for controllers, so that the triggering frequency can be reduced considerably while maintaining favorable tracking performance. Moreover, to circumvent the jumping problem and avoid the explosion of complexity in virtual controllers during backstepping design, a second-order command filter along with filtering error compensation signals are designed. Finally, the stability and tracking performance of the proposed algorithm are theoretically analyzed and validated through simulations.
Keywords:
Adaptive fuzzy model
dynamic event-triggered control
quad-rotor unmanned aerial vehicles (QUAVs)
uniform quantization
Journal
IF:
5.7
Papers:
675
Citations:
2.4W