悬停状态下小型无人直升机飞行动力学模型辨识

为了更好地研究小型无人直升机悬停状态动力学特性，对一个8.1 kg三轴陀螺仪增稳的电动直升机，从线性系统辨识方面及非线性建模方面，进行了动力学模型深入研究。在线性系统辨识过程中，应用频域辨识方法，在飞行中同时采集陀螺仪之前及之后的操纵数据进行双系统辨识。在非线性建模过程中，机体、旋翼及尾桨动力学被分别建模。尾桨动力学应用3阶段辨识法单独提取基底、陀螺仪及整体增稳模型。结合2种分析过程，应用非线性-线性模型结合修正方法，提高相互的仿真精度。结果表明:13阶高阶模型在线性辨识过程中相对比11阶模型表现更优；双系统线性模型的基底模型数据具有高质量高频特性，最高频率限制可达30 rad/s；除挥舞方程参数和尾桨参数以外，非线性数学模型（NMM）进行了7个非线性变量的修正，有效地拟合了悬停实验数据。 

Identification of flight dynamics models of a small-scale unmanned helicopter in hover condition

In order to better study the hover dynamics characteristics of small-scale unmanned helicopter, the in-depth dynamics model analysis of linear system identification and nonlinear modeling was conducted in this paper on an 8.1 kg electric helicopter with 3-axis gyro augmentation. In the linear system identification procedure, frequency-domain identification method was adopted. Double systems were obtained by using command signals from both before and after the gyro part. In the nonlinear modeling procedure, body dynamics, rotor dynamics, and tail rotor dynamics were modeled separately. The tail rotor dynamics utilized 3-stage identification method to extract the base model, the gyro model, and the overall model. A nonlinear-linear combined modification method was decided for improving the models' performance. The results show that the 13-state high-order model has higher simulation accuracy compared with the 11-state model. The flight data of the helicopter's base model for dual system linear model has high quality in high-frequency domain, and the maximum frequency is 30 rad/s. Apart from the flapping equation parameters and tail rotor parameters, the combined modification method got 7 parameters of the nonlinear mathematical model (NMM) corrected, which fits the experimental hover data effectively.