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Stabilization of Flight Controller for Unmanned Aerial Vehicle

机译:无人空中车辆飞行控制器的稳定

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This paper presents the technique to implement stability and control of multirotor unmanned aerial vehicle (UAV). The output of the inertial sensors like accelerometer and gyroscope is noisy. These sensors are used to get the orientation and attitude of the UAV. Most of the flight controllers available these days are built to fly in a very specific environment. One cannot fly with these flight controllers in heavy wind or where precise control is required. As a result, the UAV’s that use these flight controllers feel unresponsive and sloppy. The goal here is to get excellent flight dynamics and robust flight control which can fly in any conditions. The UAV’s should be able to do aggressive maneuvering, fault tolerance etc. In the proposed design, Kalman Filter algorithm is implemented to get precise values from Inertial Measurement Unit. Extended Kalman Filter (EKF) is used for sensor fusion and to get precise values from Inertial Measurement Unit PID algorithms are used to stabilize the UAV and for maneuver Rotational speed of the four motors can be increased or decreased independently to correct the orientation or position. The flight controller is built with STM32F4 ARM controller.
机译:本文介绍了实现多陆无人机(UAV)的稳定性和控制的技术。惰性传感器等加速度计和陀螺仪的输出是嘈杂的。这些传感器用于获得无人机的方向和态度。这些天的大多数飞行控制器都是在一个非常具体的环境中飞行。一个不能在大风中使用这些飞行控制器飞行,或者需要精确控制。因此,UAV将使用这些飞行控制器感到无响应和邋..这里的目标是获得出色的飞行动态和强大的飞行控制,可以在任何条件下飞行。 UAV应该能够在所提出的设计中进行积极的机动,容错等,实现卡尔曼滤波器算法以获得惯性测量单元的精确值。扩展卡尔曼滤波器(EKF)用于传感器融合,并从惯性测量单元中获得精确值PID算法用于稳定UAV,并且对于四个电动机的机动转速可以独立地增加或减小以校正方向或位置。飞行控制器采用STM32F4 ARM控制器构建。

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