事件(粒子物理)
计算机科学
控制(管理)
控制理论(社会学)
自适应控制
控制工程
工程类
人工智能
物理
量子力学
作者
Chengjie Cao,Zijuan Luo,Zhongyuan Zhao
标识
DOI:10.1177/01423312241239207
摘要
This paper proposes an event-triggered adaptive control algorithm with prescribed performance for quadrotor unmanned aerial vehicles (UAVs). The dynamic uncertainty and unknown external interference of the system are compensated by the property of approximation in a radial basis function (RBF) neural network. Based on this, introduce a performance constraint function to ensure that the trajectory tracking error of quadrotor UAV converges to a preset area, thereby guaranteeing transient and steady-state performance. In addition, design an event-triggered adaptive controller with the combination of the event-triggered mechanism, reducing the controller’s update frequency. Simulation results demonstrate that this algorithm exhibits strong anti-interference properties and effectively resolves the flight control problem of quadrotor UAV.
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