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.

翻译：近年来，非线性控制方法如模型预测控制（MPC）和强化学习（RL）在四旋翼飞行器控制领域引起了越来越多的关注。与级联PID控制器等经典控制方法不同，MPC和RL高度依赖于精确的系统动力学模型。四旋翼系统辨识过程历来繁琐，通常需要借助推力测试台等额外设备。此外，对于实现精确端到端控制至关重要的底层细节（如电机延迟）常被忽略。本研究提出一种数据驱动方法，仅基于本体感知数据来辨识四旋翼的惯性参数、推力曲线、扭矩系数以及一阶电机延迟。电机延迟的估计尤其具有挑战性，因为通常无法测量电机转速（RPM）。我们推导了一种基于最大后验概率（MAP）的方法来估计这个隐式时间常数。我们的方法仅需约一分钟的飞行数据（无需任何额外设备即可采集，通常包含三个简单机动动作）。实验结果表明，我们的方法能够准确恢复多个四旋翼飞行器的参数。该方法还有助于在恶劣的户外环境下，对大型四旋翼飞行器部署基于强化学习的端到端控制。