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Backstepping control for quadrotor with BP neural network based thrust model

机译:基于BP神经网络推力模型的四转子反推控制。

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This paper presents a nonlinear backstepping control scheme for quadrotor with BP neural network based thrust model. The thrust in quadrotor system is difficult to calculate or measure, so the paper build a BP based thrust model to approximate the mapping between thrust with factors of altitude, voltage and the high level time of PWM. Through the model, the control input for each rotor can be calculate accurately to gain the desired thrust. The controller is designed by backstepping method and verified by Lyapunov stability theorem. Hovering experimental results are presented to show the effectiveness of the proposed controller.
机译:提出了基于BP神经网络推力模型的四旋翼非线性反步控制方案。四旋翼系统中的推力难以计算或测量,因此本文建立了一个基于BP的推力模型,以近似推力与高度,电压和PWM高电平时间之间的映射关系。通过该模型,可以精确计算每个转子的控制输入,以获得所需的推力。控制器采用反推法设计,并经Lyapunov稳定性定理验证。悬停的实验结果表明了所提出的控制器的有效性。

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