Direct current geared motor data: Voltage, current, and speed measured under different experimental conditions

Area of Science:

• Robotics and Control Systems
• Electrical Engineering
• Data Science

Background:

• Automated Guided Vehicles (AGVs) require precise actuator control.
• DC motors are common actuators in AGVs, but their dynamic behavior needs thorough characterization.
• Accurate system identification is crucial for developing effective control strategies.

Purpose of the Study:

• To provide comprehensive datasets from laboratory tests of DC motors intended for AGV applications.
• To facilitate system modeling and identification of motor dynamics.
• To support the design of advanced control systems for AGVs and mobile robots.

Main Methods:

• Collected eleven datasets from individual laboratory tests of two identical DC motor models.
• Measured twelve key variables including time, encoder counts, velocity, current, voltage, applied potential difference, motor status, and PWM output.
• Ensured consistent data structure across all collected datasets.

Main Results:

• Generated eleven structured datasets detailing DC motor performance under various conditions.
• The datasets capture essential dynamic variables for system identification.
• Data includes measurements of voltage, current, velocity, and encoder pulses.

Conclusions:

• The presented datasets are valuable for modeling and identifying the dynamic characteristics of DC motors for AGVs.
• Utilizing this data can enhance the accuracy of control systems, particularly for AGV position control.
• The data also serves as a resource for robot design studies and mobile robot/AGV simulations.