论文标题
实时系统识别使用深度学习的线性过程，并应用于无人机
Real-time System Identification Using Deep Learning for Linear Processes with Application to Unmanned Aerial Vehicles
论文作者
论文摘要
本文提出了一种使用深度学习（DL）和修改后的继电器反馈测试（MRFT）的新型参数识别方法。提出的方法利用MRFT揭示了有关未知过程的频率。然后将其传递给训练有素的DL模型，以识别基础过程参数。提出的方法分别保证了识别和控制阶段的稳定性和性能，并且需要几秒钟的观察数据来推断动态系统参数。在模拟和实验中使用了四型无人机（UAV）态度和高度动力学，以验证提出的方法。结果表明，所提出的方法的有效性和实时功能在准确性，偏见，计算效率和数据要求方面优于常规预测错误方法。
This paper proposes a novel parametric identification approach for linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT). The proposed methodology utilizes MRFT to reveal distinguishing frequencies about an unknown process; which are then passed to a trained DL model to identify the underlying process parameters. The presented approach guarantees stability and performance in the identification and control phases respectively, and requires few seconds of observation data to infer the dynamic system parameters. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented methodology. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.
