Velocity PID(F) Best Practices - To Integrate, or Not To Integrate?

[AustinSchuh]

Originally Posted by Oblarg

What kind of filtering do people typically use? Would exponential smoothing suffice?

I'm pretty certain that 971 is not typical, but we run either a statespace observer or kalman filter to smooth the velocity out (Practically speaking, they are just different ways to do the same thing with different knobs). Most teams seem to just use PIDF (if they use anything at all).

The math for an observer boils down to

X_hat(n + 1) = A X_hat(n) + B u(n) + L * (Y - C X_hat(n))

A and B are derived from your model. C is the mapping from your state to your sensor readings. X_hat is the estimate of the state. Y is your sensor measurement, and L is the feedback gain that you get from tuning the filter.

I'm happy to go into more details if you are interested, and our code to implement this is publically available.