Can anyone point to any good resources on these. That is to say algorithms for motor control when there is backlash (wobble) within a gear chain.
In my circumstances I've got the motor itself, which feeds from the actual DC motor, through the motor's built in gearbox (which will have some backlash), to an output shaft. The output shaft then powers one gearbox which feeds rotation to an encoder, and powers a seperate gearbox which gears down a bit further before the final output. So the place I measure at is both some backlash away from the motor's internals, and some backlash away from the output.
Does anyone know of any resources about the sort of algorithms which could let me calculate backlash reduction methods which, during operation (at potentially many different torque loads), can take input data only from that encoder, although some amount of "training" (I'm not talking AI here so much as taking measurements beforehand at other points on the shafts system) with measurements at the motor, the output shaft and the encoder shaft would be possible (but only at one or two torque levels, not at all the levels the system could find itself handling).
Thanks
P.S. I'm talking about practically focused resources, not highly abstracted academic papers, and I'm mroe interested in reading things tounderstand this and implement my own code for it, rather than full existing libraries which do it all but don'texplain how they do it. I understand this sort of anti-backlash method occurs sometimes within industrial servo units where they use multiple encoders, one at the motor itself and one at the actual output shaft, but what about circumstances where you're trying to extrapolate knowledge of these from an encoder separate from them by some slightly backlashy gears already.