A Look at Sensorless Homing: Stepper Motor Control Without End Switches
Stepper motors play a crucial role in various industrial and DIY applications, providing precise control over movement in CNC machines, 3D printers, and robotic arms. Typically, these motors rely on external sensors or end switches to detect their home position or stop movement at a predefined point. These sensors add cost and complexity to the setup, requiring additional wiring and calibration. However, there is an alternative method that eliminates the need for physical sensors—sensorless homing. This technique, though available for years in stepper motor drivers like the Trinamic TMC2209, remains relatively underutilised due to its complexity and sensitivity to speed variations. Andrea Favero's latest project showcases sensorless homing and centring of a stepper motor using a Raspberry Pi Pico and a TMC2209 driver. His setup leverages the StallGuard function of the TMC2209, which detects changes in motor torque via UART communication. By monitoring the decrease in StallGuard values when the motor encounters resistance, the system can determine a home position without external sensors. The RP2040 microcontroller, running MicroPython, counts the steps between two mechanical stops and automatically centres the motor at the midpoint. The project effectively demonstrates how a simple yet precise homing system can be implemented using software alone.
One of the standout features of this project is the use of the RP2040’s Programmable I/O (PIO) for generating and counting step pulses, ensuring smooth operation and accurate positioning. The homing speed is adjustable, with the best results observed between 400Hz and 1200Hz. A push button triggers the homing sequence, and an onboard RGB LED provides real-time feedback, flashing red when the StallGuard threshold is crossed and green when the motor reaches the centre. Despite the clear advantages of sensorless homing—cost savings, reduced wiring, and improved reliability—it has yet to gain widespread adoption. The method's reliance on fine-tuned parameters and speed-dependent behaviour makes it less straightforward than traditional sensor-based approaches.
For those interested in experimenting with this approach, Andrea has made the source code available on his GitHub repository, offering a valuable resource for makers and engineers looking to integrate sensorless homing into their projects.
