用在线参数估计提升四轴飞行器在噪声下的稳定性。
Online parameter estimation for the Crazyflie quadcopter through an EM algorithm
- 基于EM算法实现飞行器参数的在线估计。
- 在线估计收敛范围比离线方法略大。
- 适合做无人机自适应控制的研究者参考。
近年来，无人机因其体积小、成本低、运行可靠而日益普及。它们配备多种传感器，可执行各类飞行任务，到达人类难以进入的区域。地震常造成基础设施损毁，使救援人员无法抵达某些区域，而无人机则能提供帮助。此外，无人机在航拍、农业喷洒及物资运输中也发挥着重要作用。本文研究了四旋翼无人机系统，引入随机噪声以分析其对飞行器系统的影响。采用扩展卡尔曼滤波器基于传感器噪声观测进行状态估计，并基于随机微分方程（SDE）系统设计了线性二次高斯控制器。通过期望最大化（EM）算法实现飞行器参数估计，对比了离线与在线参数估计的结果。实验表明，在线参数估计具有略大的收敛范围，验证了其在动态环境中的鲁棒性。
原文摘要 · Abstract (English)
Drones are becoming more and more popular nowadays. They are small in size, low in cost, and reliable in operation. They contain a variety of sensors and can perform a variety of flight tasks, reaching places that are difficult or inaccessible for humans. Earthquakes damage a lot of infrastructure, making it impossible for rescuers to reach some areas. But drones can help. Many amateur and professional photographers like to use drones for aerial photography. Drones play a non-negligible role in agriculture and transportation too. Drones can be used to spray pesticides, and they can also transport supplies. A quadcopter is a four-rotor drone and has been studied in this paper. In this paper, random noise is added to the quadcopter system and its effects on the drone system are studied. An extended Kalman filter has been used to estimate the state based on noisy observations from the sensor. Based on a SDE system, a linear quadratic Gaussian controller has been implemented. The expectation maximization algorithm has been applied for parameter estimation of the quadcopter. The results of offline parameter estimation and online parameter estimation are presented. The results show that the online parameter estimation has a slightly larger range of convergence values than the offline parameter estimation.
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