基于可视化神经网络的鸽子柔性扑翼振动控制。

Hejia Gao, Jinxiang Zhu, Changyin Sun, Zi-Ang Li, Qiuyang Peng
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引用次数: 0

摘要

这项研究调查了类似鸽子的柔性扑翼,这种扑翼以其低能耗、高灵活性和轻量化设计而闻名。然而,这种柔性扑翼系统在飞行过程中容易发生变形和振动,导致性能下降。因此,有必要设计一种有效控制柔性机翼振动的控制方法。本文提出了一种改进的刚性有限元法(IRFE)来建立柔性扑翼的动态可视化模型。随后,设计了基于非奇异终端滑模(NTSM)控制和模糊神经网络(FNN)的自适应振动控制器,有效地解决了系统不确定性和执行器失效问题。利用所提出的控制方法,在李雅普诺夫稳定性理论的背景下实现了闭环系统的稳定性。最后,利用MapleSim和MATLAB/Simulink进行了联合仿真,验证了所提控制器在轨迹跟踪和振动抑制方面的有效性和鲁棒性。研究结果表明,该方法在军事(低空侦察、城市作战、精准投放等)和民用(野外考察、监测、救灾等)应用中具有重要的实用价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Visualized neural network-based vibration control for pigeon-like flexible flapping wings.

This study investigates pigeon-like flexible flapping wings, which are known for their low energy consumption, high flexibility, and lightweight design. However, such flexible flapping wing systems are prone to deformation and vibration during flight, leading to performance degradation. It is thus necessary to design a control method to effectively manage the vibration of flexible wings. This paper proposes an improved rigid finite element method (IRFE) to develop a dynamic visualization model of flexible flapping wings. Subsequently, an adaptive vibration controller was designed based on non-singular terminal sliding mode (NTSM) control and fuzzy neural network (FNN) in order to effectively solve the problems of system uncertainty and actuator failure. With the proposed control, stability of the closed loop system is achieved in the context of Lyapunov's stability theory. At last, a joint simulation using MapleSim and MATLAB/Simulink was conducted to verify the effectiveness and robustness of the proposed controller in terms of trajectory tracking and vibration suppression. The obtained results have demonstrated great practical value of the proposed method in both military (low-altitude reconnaissance, urban operations, and accurate delivery, etc.) and civil (field research, monitoring, and relief for disasters, etc.) applications.

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