Quote:
Originally Posted by Jared Russell
What are your typical control loop rates, and what states are you generally tracking in your formulation (position/velocity/acceleration?). In the case of the higher order derivatives, are you filtering the measurements at all (in your observers or otherwise)?
I understand state space control, but have found that getting a smooth velocity/acceleration signal to be one of the "dark arts" when I have played with it in the past.
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We run our control loops at 100 hz , and our data comes in at 500 hz. I was going to run that through a kalman filter at that rate, but nothing really needs anything that overkill, and I never got around to implementing it.
Our drivetrain is 1 control loop. Our states are [left position; left velocity; right position; right velocity].
Our shooter is 3 states. [observed voltage, position, velocity]. Our shooter knows if the spring is attached to the pusher or not, and gain schedules the shooter model and gains accordingly. The origin of the shooter is internally set to the virtual 0 length position of the spring. I really dislike implementing integral control and dealing with integral windup, so I like to implement things using what my professor called Delta U control. The trick is to add an integrator to the plant (so that you model it as taking in changes in power), and then have the observer estimate the actual power being applied. This means that when the system is responding like it is supposed to, the integral term doesn't wind up.
Our claw is 4 states and 1 control loop. [bottom position, bottom velocity, separation position, separation velocity]. This lets us control what we actually care about.
I have found that to get clean velocity estimates, you need to slow down the observer and trust the model more. An observer with really slow poles decays error in the estimate very slowly. An observer with really fast poles decays error in the estimates very quickly. The end result is that noise in the input signal gets amplified when passed through an observer with fast poles, and filtered when passed through an observer with slow poles. If you formulate the problem with a kalman filter instead and look at the gain matrix that the kalman filter is using, you can see how increasing the noise in the input signals will slow down the poles.