Earlier quoted context omitted.
I feel like people overcomplicate even the "simple" explanations like the OPs and this one. Basically, a Kalman filter is part of a larger class of "estimators", which take the input data, and run additional processing on top of it to figure out the true measurement. The very basic estimator a low pass filter is also an "estimator" - it rejects high frequency noise, and gives you essentially a moving average. But is…
Interesting. It sounds like you ended up with a data-driven estimator. Did you have a chance to compare the data-driven and model-based approaches?
When doing Kalman filters, you usually have the basic form of the dynamics in the linear system, but the coefficients are usually determined experimentally (since things like mass is hard to estimate)
Additionaly, because i have direct integrator control (i.e when my target is at setpoint, my control input is 0), all I need is a proportional gain that is small enough for the system to not go unstable. And i have a physical low pass filter of the motor rotor inertia.