CNN based precise nonlinear tracking control for a nano unmanned helicopter: Theory and implementation.

Xun Gu, Bin Xian, Mohan Liu, Aochen Ma
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引用次数: 0

Abstract

This paper proposes a deep convolutional neural network (CNN)-based geometric integral control strategy for a nano unmanned helicopter, which weighs less than 70 g and has a fuselage length of less than 0.25 m. Compared to existing nonlinear controllers, the proposed method offers significant advantages in terms of feasibility, easy tuning of parameters, and data requirements. The deep CNN-based system identification effectively captures the complex dynamics of the nano helicopter, enabling accurate modeling and compensation for uncertainties. The geometric integral control strategy enhances the system's robustness against unknown external disturbances, and ensures stable and precise flight performance. The feasibility of the proposed method is demonstrated through real-time flight experiments, which show strong robustness and accurate trajectory tracking performance. Additionally, the tuning process is simplified due to the adaption nature of the deep learning-based approach, reducing the need for extensive parameter adjustments. The data requirements are also minimized, as the deep CNN can be trained with a relatively small dataset, making the method more practical for real-world applications. The results indicate that the proposed method outperforms traditional control strategies, particularly in terms of handling modeling uncertainties and external disturbances.

基于CNN的纳米无人直升机精确非线性跟踪控制:理论与实现。
针对重量小于70 g、机身长度小于0.25 m的纳米无人直升机,提出了一种基于深度卷积神经网络(CNN)的几何积分控制策略。与现有的非线性控制器相比,该方法在可行性、参数易整定和数据要求等方面具有显著的优势。基于cnn的深度系统识别有效地捕获了纳米直升机的复杂动力学,实现了精确的建模和不确定性补偿。几何积分控制策略增强了系统对未知外部干扰的鲁棒性,保证了系统稳定精确的飞行性能。通过实时飞行实验验证了该方法的可行性,具有较强的鲁棒性和准确的轨迹跟踪性能。此外,由于基于深度学习的方法的自适应特性,调整过程得到了简化,减少了大量参数调整的需要。数据需求也被最小化,因为深度CNN可以用相对较小的数据集进行训练,使该方法更适合实际应用。结果表明,该方法在处理建模不确定性和外部干扰方面优于传统控制策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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