Abstract
Recently non-linear control methods like Model Predictive Control (MPC) and Reinforcement Learning (RL) have attracted increased interest in the quadrotor control community. In contrast to classic control methods like cascaded PID controllers, MPC and RL heavily rely on an accurate model of the system dynamics. The process of quadrotor system identification is notoriously tedious and is often pursued with additional equipment like a thrust stand. Furthermore, low-level details like motor delays which are crucial for accurate end-to-end control are often neglected. In this work, we introduce a data-driven method to identify a quadrotor's inertia parameters, thrust curves, torque coefficients, and first-order motor delay purely based on proprioceptive data. The estimation of the motor delay is particularly challenging as usually, the RPMs can not be measured. We derive a Maximum A Posteriori (MAP)-based method to estimate the latent time constant. Our approach only requires about a minute of flying data that can be collected without any additional equipment and usually consists of three simple maneuvers. Experimental results demonstrate the ability of our method to accurately recover the parameters of multiple quadrotors. It also facilitates the deployment of RL-based, end-to-end quadrotor control of a large quadrotor under harsh, outdoor conditions.
Abstract (translated)
近年来，在 quadrotor 控制社区中，非线性控制方法如 Model Predictive Control（MPC）和 Reinforcement Learning（RL）引起了越来越多的关注。与经典的控制方法（如级联 PID 控制器）相比，MPC 和 RL 更依赖于对系统动态的准确建模。 quadrotor 系统识别的过程臭名昭著，通常需要使用诸如推力台等额外的设备进行追求。此外，低级别的细节，如电机延迟，对于准确的全局控制往往被忽视。在本文中，我们介绍了一种基于惯性数据的数据驱动方法，用于仅基于本体感知数据识别 quadrotor 的惯量参数、推力曲线、扭矩系数和第一阶电机延迟。估计电机延迟尤其具有挑战性，因为通常无法测量转速。我们推导出一种基于最大后验概率（MAP）的方法来估计隐含时间常数。我们的方法仅需要大约一分钟的飞行数据，无需任何额外设备，通常包括三个简单的操作。实验结果证明，我们的方法能够准确恢复多个 quadrotor 的参数。同时，它还有助于在恶劣的户外条件下部署基于 RL 的全quadrotor 控制。
URL
https://arxiv.org/abs/2404.07837
