Industrial robots, collaborative robots, humanoid robots, and medical robots rely on tightly integrated joint systems. A typical robotic joint may contain a permanent magnet motor, gearbox, position encoder, torque sensor, brake, bearing, power electronics, and controller.
Magnetic field components are used not only to generate motor torque but also to measure position, speed, current, torque, and proximity. High robot performance therefore depends on how effectively the motor magnetic circuit, magnetic sensors, mechanical transmission, and control algorithm work together.
I. What Magnetic Components Are Used in Robots?
| Magnetic Component | Main Function | Common Paired Component |
| Rotor permanent magnets | Interact with the stator field to produce torque | Brushless DC or permanent magnet synchronous motor |
| Encoder magnet or magnetic ring | Provides angular position and speed information | Hall, AMR, GMR, or TMR sensor |
| Current-sensing magnetic core | Concentrates the magnetic field generated by phase current | Hall-effect or magnetoresistive sensor |
| Magnetic brake assembly | Holds the joint after power is removed | Electromagnetic brake |
| Magnetoelastic torque structure | Converts mechanical stress into a measurable magnetic change | Magnetic or magnetoresistive sensor |
| Flux guide and magnetic shield | Controls leakage flux and field distribution | Soft magnetic alloy, pure iron, or ferrite |
These components cannot be designed independently. Leakage flux from the motor may enter the encoder area, while inverter current and power cables may create additional alternating magnetic fields around sensitive sensors.
II. How Do Motors and Sensors Form a Control Loop?
A robotic joint generally uses several nested feedback loops. The current loop is the fastest and regulates electromagnetic torque. The speed loop uses encoder information to control rotational velocity, while the position loop compares the commanded and actual joint angles.
Torque sensors or output-shaft encoders may provide additional feedback about gearbox deformation, backlash, external force, or contact with the environment.
High-precision joints often include one encoder at the motor rotor and another at the output shaft. The motor-side encoder supports commutation and fast motor control. The output encoder measures the actual joint position after the gearbox and can reveal transmission error, torsional deformation, and backlash.
| Feedback Signal | Main Control Function | Typical Design Requirement |
| Phase current | Electromagnetic torque control | High bandwidth and low offset |
| Rotor position | Motor commutation | Accurate electrical angle |
| Rotor speed | Dynamic speed regulation | Low noise and short delay |
| Output angle | True joint position control | High resolution and good repeatability |
| Joint torque | Force and compliance control | Low drift and overload resistance |
| Temperature | Thermal protection and compensation | Correct placement and fast response |
III. What Problems Can Magnetic Interference Cause?
1. Encoder Angle Error
Magnetic encoders calculate angle from field direction or field components. Misalignment, changing air gaps, magnet eccentricity, and motor leakage flux can create offset, nonlinearity, and periodic angle error.
Even when an encoder has high nominal resolution, installation error may limit the true system accuracy.
2. Current and Torque Estimation Error
External magnetic fields, core remanence, temperature drift, and conductor position can shift the zero point of magnetic current sensors.
Because motor torque is often calculated from phase current, a current measurement error can directly become a torque-control error.
3. Local Magnetic Saturation
Flux guides and shielding parts must have sufficient cross-sectional area and saturation capability. If a local region saturates, magnetic flux no longer increases linearly with current.
Saturation may reduce motor torque, distort sensor signals, increase harmonics, and cause unexpected heating.
4. Temperature-Related Drift
Permanent magnet flux, winding resistance, Hall sensitivity, magnetic sensor gain, and soft magnetic permeability all change with temperature.
If temperature effects are ignored, a joint calibrated at room temperature may produce errors after continuous high-load operation.
IV. How Can Motors and Magnetic Sensors Be Efficiently Coordinated?
1. Conduct Joint Electromagnetic Simulation
Motor simulation should evaluate more than torque, efficiency, and back electromotive force. Designers should also calculate leakage fields around encoders, brakes, current sensors, and signal wiring.
Electromagnetic analysis can be combined with thermal, structural, and control models. This helps determine whether sensor errors will increase when the motor becomes hot, the shaft deflects, or the bearing air gap changes.
2. Optimize Magnet and Sensor Placement
The encoder magnet should be accurately centered on the rotating shaft, and the sensor air gap should remain within its specified range.
Where motor leakage is strong, designers can increase separation, add a flux return path, use differential sensing, or install a magnetic shield. Shielding thickness should be calculated because a saturated shield may lose effectiveness.
3. Synchronize Signal Acquisition
Current, position, speed, torque, and temperature data should be sampled with consistent timing. Unsynchronized measurements force the controller to combine states from different moments, reducing dynamic accuracy.
Precise time stamping and deterministic communication are especially important in multi-axis robots.
4. Calibrate the Complete Joint
A motor and encoder may each meet their individual specifications, yet the assembled joint may still show significant error.
Complete joint calibration should include mechanical zero, magnetic offset, eccentricity compensation, torque constant, friction, gearbox transmission error, and temperature drift.
5. Use Sensor Fusion
Combining motor current, dual encoder data, torque sensing, inertial measurement, and temperature information allows the controller to distinguish between actual load changes and sensor errors.
Sensor fusion can also improve fault detection. For example, a disagreement between motor current and measured joint torque may indicate gearbox damage, mechanical blockage, or calibration drift.
V. How Should Magnetic Materials Be Selected?
Rotor magnets should be selected according to torque density, operating temperature, reverse-field resistance, cost, and corrosion conditions. Neodymium-iron-boron provides high magnetic energy, samarium-cobalt offers strong high-temperature stability, and ferrite can be suitable for cost-sensitive designs with sufficient space.
Electrical steel, soft magnetic composites, pure iron, or high-permeability alloys may be used for stators, flux guides, shields, and sensor structures.
A higher-grade magnet does not automatically improve the complete robot. Stronger magnets may increase leakage flux, sensor interference, mechanical attraction, and cost. Material selection must be coordinated with magnetic circuit geometry and control requirements.
Conclusion
Efficient cooperation between robotic magnetic components, motors, and sensors requires a complete chain from magnetic field generation and mechanical output to state measurement and closed-loop control.
Joint simulation, controlled leakage flux, accurate sensor placement, synchronized sampling, dual-encoder feedback, temperature compensation, and complete-system calibration can reduce angle error and torque ripple.
The best robotic joint is not simply the one with the strongest magnet or highest-resolution sensor. It is the one in which magnetic, mechanical, electronic, and control components are designed as one coordinated system.
