Almost two years ago, a research team showed that it was possible to get fine motor control from cheap, brushless DC motors. Normally this is not feasible because the motors are built-in such a way that the torque applied is not uniform for every position of the motor, a phenomenon known as “cogging”. This is fine for something that doesn’t need low-speed control like a fan motor, but for robotics it’s a little more important. Since that team published their results, though, we are starting to see others implement their own low-speed brushless motor controllers.
The new method of implementing anti-cogging maps out the holding torque required for any position of the motor’s shaft so this information can be used later on. Of course this requires a fair amount of calibration; [madcowswe] reports that this method requires around 5-10 minutes of calibration. [madcowswe] also did analysis of his motors to show how much harmonic content is contained in these waveforms, which helps to understand how this phenomenon arises and how to help eliminate it.
While [madcowswe] plans to add more features to this motor control algorithm such as reverse-mapping, scaling based on speed, and better memory usage, it’s a good implementation that has visible improvements over the stock motors. The original research is also worth investigating if a cheaper, better motor is something you need.
Cheap, brushless motors may be the workhorses behind our RC planes and quadcopters these days, but we’ve never seen them in any application that requires low-speed precision. Why? Sadly, cheap brushless motors simply aren’t mechanically well-constructed enough to offer precise position control because they exhibit cogging torque, an unexpected motor characteristic that causes slight variations in the output torque that depend rotor position. Undaunted, [Matthew Piccoli] and the folks at UPenn’s ModLab have developed two approaches to compensate and minimize torque-ripple, essentially giving a cheap BLDC Motor comparable performance to it’s pricier cousins. What’s more, they’ve proven their algorithm works in hardware by building a doodling direct-drive robotic arm from brushless motors that can trace trajectories.
Cogging torque is a function of position. [Matthew’s] algorithm works by measuring the applied voltage (or current) needed to servo the rotor to each measurable encoder position in a full revolution. Cogging torque is directional, so this “motor fingerprint” needs to be taken in both directions. With these measured voltages (or currents) logged for all measurable positions, compensating for the cogging torque is just a matter of subtracting off that measured value at any given position while driving the motor. [Matthew] has graciously taken the trouble of detailing the subtleties in his paper (PDF), where he’s actually developed an additional acceleration-based method.
Hobby BLDC motors abound these days, and you might even have a few spares tucked away on the shelf. This algorithm, when applied on the motor controller electronics, can give us the chance to revisit those projects that mandate precise motor control with high torque–something we could only dream about if we could afford a few Maxon motors. If you’re new to BLDC Motor Control theory, check out a few projects of the past to get yourself up-and-running.
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