Quote:
Originally Posted by ewhitman
A Kalman Filter is not a substitute for a PID. It is something you might do in addition to a PID.
A Kalman Filter attempts to accurately estimate the state (position, velocity, etc.) of your system. It is used either when your sensors are inaccurate (always true to some extent) or when you do not have enough sensors to measure your full state directly.
A KF with even a very crude model can provide more accurate state estimates than reading your sensors directly or traditional filtering if the KF is tuned correctly (not necessarily easy to do). A more accurate system model will result in correspondingly more accurate state estimates.
Extended Kalman Filters allow you to use nonlinear models of your system, which is sometimes desirable, but significantly complicates the process.
I would say that a KF is almost always overkill for FRC applications.
A PID is a control strategy, which means it determines actuator commands (usually motor voltage or duty cycle in an FRC context) based on the system state.
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I know, I know, I have both of them implemented already. I guess my grammar was a bit off. I was using the KF to get a more accurate estimate than trying to rely on the encoders that can be off my about 50 degrees. What I meant was that teams did fine using noisy sensors with just the proportional portion of the PID controller. So if having the I and D components is overkill, a Kalman Filter is way overkill... Eh, it was fun writing those though, a great learning experience.