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首页> 美国卫生研究院文献>Philosophical transactions. Series A Mathematical physical and engineering sciences >A wavelet neural control scheme for a quadrotor unmanned aerial vehicle
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A wavelet neural control scheme for a quadrotor unmanned aerial vehicle

机译：四旋翼无人机的小波神经控制方案

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Wavelets are designed to have compact support in both time and frequency, giving them the ability to represent a signal in the two-dimensional time–frequency plane. The Gaussian, the Mexican hat and the Morlet wavelets are crude wavelets that can be used only in continuous decomposition. The Morlet wavelet is complex-valued and suitable for feature extraction using the continuous wavelet transform. Continuous wavelets are favoured when high temporal and spectral resolution is required at all scales. In this paper, considering the properties from the Morlet wavelet and based on the structure of a recurrent high-order neural network model, a novel wavelet neural network structure, here called a recurrent Morlet wavelet neural network, is proposed in order to achieve a better identification of the behaviour of dynamic systems. The effectiveness of our proposal is explored through the design of a decentralized neural backstepping control scheme for a quadrotor unmanned aerial vehicle. The performance of the overall neural identification and control scheme is verified via simulation and real-time results.This article is part of the theme issue ‘Redundancy rules: the continuous wavelet transform comes of age’.
机译：小波被设计为在时间和频率上都具有紧凑的支持，从而使它们能够在二维时频平面中表示信号。高斯，墨西哥帽和Morlet小波是粗小波，只能用于连续分解。 Morlet小波是复数值，适合使用连续小波变换进行特征提取。当在所有尺度上都需要较高的时间和频谱分辨率时，连续小波将受到青睐。本文考虑了Morlet小波的性质，并基于递归高阶神经网络模型的结构，提出了一种新颖的小波神经网络结构，这里称为递归Morlet小波神经网络，以达到更好的效果。识别动态系统的行为。我们的建议的有效性是通过设计四旋翼无人机的分散神经反推控制方案来探索的。通过仿真和实时结果验证了整个神经识别和控制方案的性能。本文是主题“冗余规则：连续小波变换的时代”的一部分。

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