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对具有独特飞行特性的无人动力伞进行了研究,建立了无人动力伞9自由度非线性动力学模型。针对无人动力伞飞行特性具有的不确定性、非线性和复杂性特点,研究了基于观测器/卡尔曼滤波辨识算法和直接状态空间模型辨识算法。根据系统的飞行数据,辨识得到系统的纵向状态空间模型,分析了2种算法的辨识速度和辨识精度。辨识模型的仿真结果表明了辨识算法的可行性和有效性。
Unmanned dynamic parachute with unique flight characteristics was studied, and a 9-DOF nonlinear dynamic model of unmanned powered parachute was established. Aiming at the uncertainty, nonlinearity and complexity characteristics of unmanned powered flight, the recognition algorithms based on observer / Kalman filter and direct state space model are studied. According to the flight data of the system, the vertical state space model of the system is identified and the recognition speed and identification accuracy of the two algorithms are analyzed. Simulation results show that the identification algorithm is feasible and effective.