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Distributed Artificial Neural Networks-Based Adaptive Strictly Negative Imaginary Formation Controllers for Unmanned Aerial Vehicles in Time-Varying Environments

机译:基于人工神经网络的自适应严格负虚构的虚构模块,用于无人驾驶的空中车辆在时变环境中

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摘要

Formation control techniques have been widely implemented in networked multirobot systems. In this article, we present a novel framework for swarm multiagent systems based on the relative-position output feedback consensus supported with the new concept of adaptive strictly negative imaginary consensus controllers, leveraging the learning capability of artificial neural networks. For experimental validation, we consider the case of two quadcopters moving together while carrying a dynamic load. We employ Kharitonov's theorem to study the stability of the proposed adaptive control systems. Finally, a rigorous real-time experimental study is conducted to highlight the merits of the proposed formation control algorithms.
机译:形成控制技术已广泛实现在网络多电机系统中。在本文中,我们为基于相对位置输出反馈共识的基于适应性严格的虚构的虚构共识控制器的新概念,利用人工神经网络的学习能力来提出一部基于相对位置输出反馈共识的新颖框架。对于实验验证,我们考虑两个Quadcopters在一起移动的Quadcopters,同时携带动态负载。我们聘请了Kharitonov的定理来研究所提出的自适应控制系统的稳定性。最后,进行了严格的实时实验研究,以突出所提出的形成控制算法的优点。

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