DC Motor Step Response

This dataset was generated from the experimental work reported in “Parameter Identification of Motors by Cuckoo Search Using Steady-State Relations,” published in IEEE Access, vol. 9, pp. 72017–72024, 2021, doi: 10.1109/ACCESS.2021.3078578. The experimental data were developed by Miguel-Ángel Hernández-Paredes from the Instituto Tecnológico Superior de Huichapan and Dr. Omar Rodríguez Abreo from the Universidad Autónoma de Querétaro. The dataset contains real time-domain measurements acquired from DC motor experiments, including voltage, current, and angular speed signals. The main objective of the original study was to estimate the physical parameters of DC motors, namely armature resistance R, armature inductance L, torque/back-emf constant K, rotor inertia J, and viscous friction coefficient B. The experimental signals were obtained using a custom-built data acquisition system and were used for physics-based parameter identification and validation of DC motor dynamic models. Each dataset corresponds to a different motor or experimental condition, such as CML-050 and RMCS2004, making the data useful for evaluating identification methods under real hardware conditions. This dataset may be used for: Grey-box or physics-based parameter identification of DC motors. Time-domain validation of dynamic models. Development and testing of physics-informed machine learning models, including PINNs and hybrid neural networks. Benchmarking optimization and identification algorithms using noisy experimental data from real electromechanical systems.本数据集源自发表于IEEE Access 2021年第9卷第72017–72024页、题为《基于稳态关系的布谷鸟搜索算法实现电机参数辨识》（Parameter Identification of Motors by Cuckoo Search Using Steady-State Relations）的实验研究，DOI为10.1109/ACCESS.2021.3078578。 该实验数据由来自韦奇潘帕高等技术学院（Instituto Tecnológico Superior de Huichapan）的米格尔·安赫尔·埃尔南德斯-帕雷德斯（Miguel-Ángel Hernández-Paredes）与克雷塔罗自治大学（Universidad Autónoma de Querétaro）的奥马尔·罗德里格斯·阿布雷奥博士共同构建。 本数据集包含从直流电机实验中采集的真实时域测量数据，涵盖电压、电流与角速度信号。原研究的核心目标是估算直流电机的物理参数，具体包括电枢电阻R、电枢电感L、转矩/反电动势常数K、转子转动惯量J以及粘性摩擦系数B。 实验信号通过定制化数据采集系统获取，用于基于物理原理的直流电机动态模型参数辨识与验证。每一组数据集对应一台不同的电机或不同的实验工况，例如CML-050与RMCS2004，因此该数据集可用于在真实硬件环境下评估参数辨识方法的性能。 本数据集可应用于以下场景： 1. 直流电机的灰箱或基于物理原理的参数辨识； 2. 动态模型的时域验证； 3. 开发与测试物理信息机器学习模型，包括物理信息神经网络（Physics-Informed Neural Networks, PINNs）与混合神经网络； 4. 基于真实机电系统含噪实验数据，对优化与辨识算法进行基准测试。