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.