利用代数反演技术确定无人机状态空间旋转动力学模型

J. Muliadi, B. Kusumoputro
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引用次数: 1

摘要

充分的无人机动力学模型对于成功的空中任务是重要的。这样足够的飞行动力学模型是装配一个好的飞行控制器所必需的。出于建模的目的,状态空间方法已经应用于各种系统动力学中。在传统的建模中,无人机的状态空间是由第一原理构建的,它涉及到测量的努力,并处理传感器读取的不确定性和误差。因此,本文提出了一种直接从飞行数据中识别无人机状态空间的简化方法。飞行数据被直接用于克服不确定性问题。结果表明,与以往的状态空间建模技术相比,该方法能够满足惯性矩测量的要求。
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Determining the UAV State Space Rotational Dynamics Model Using Algebraic Inversion Technique
Adequate models of UAV's dynamics were important for a successful aerial mission. Such adequate model of flight dynamics were required to assemble a good flight controller. For modeling purposes, the state space methods have been applied in various system dynamics. In the conventional modeling, the UAV's state space constructed from the first principle which involved efforts of measurement and deals with uncertainties and errors in sensors reading. Hence, this work proposes a simplified method to identify the UAV state space directly from its flight data. The flight data were directly used to overcome the uncertainties issues. As the conclusion, this method were able to ommit the requirement for moments of inertia measurement compared to previous technique of State Space modeling.
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