PID question

I confess to some surprise at not understanding the difference between control signal and error signal. PID stands for proportional, integral, and differential components for the the control process. Using an error signal for the PID input is a fools errand. Properly used, proportional sets the final response, integral provides smoothing, and differential provides response speed. Direct error signals are not useful.

This is the underlying concepts for "Kalman filtering" which take into account device response characteristics to determine optimal control outputs for the highest performance and reliable stability.

The control signal is usually the output from the error amplifier that drives some control device (pass transistor, in this case)

This depends on whether the setpoint is changing or fixed, and whether or not you want fastest response to both setpoint changes and load disturbances, or just fast response to load changes and slow, over damped response to setpoint changes (like turning the voltage setting knob on the supply). There are several useful combinations.

Integral provides eventual perfection, since it keeps changing the output (ever more slowly) till the average error is zero.

Another way to look at PID response is as a soft notch filter. Any feedback process develops a conjugate pair of poles that represent the onset of oscillation (a peak in the feedback response. If you can center the response notch of the PID combination (falling gain with rising frequency from the integrator term to some minimum gain from the proportional term, with rising gain above that minimum from the derivative term) this response peak, you extend amount of closed loop gain tolerated and the frequency range of stability. Some versions of PID terms have deeper possible notches than others. The more deeply notched versions work better with inherently resonant systems.

Lots of useful lead lag phase compensation networks used to tweak feedback systems can be described as a P. I. and/or D. terms.

I think that Kalman filter control also takes the noise of the measured variable into account, combining some part of the control output run through a system model that predicts the result of control changes with some fraction of the measurement of the system, to improve the control you can achieve based strictly on a noisy measurement. Ideally, the proportions of predicted response and measured response adjust in response to the noise. Please correct me if I am misunderstanding it.

Yes, i am suggesting that OP move across the error amp to the reference value / input.

Yes, PID controllers have the added ability to change the P, I, and D values in a running system.

Doesn't seem like it to me.

(snip)

See:

Nice links, the wiki still hits the hard math a little too quick and not enough explanation. The second link is really good. I could probably use it to learn the deep stuff that is hard follow in the wiki.

I have been reading about Kalman filters for a long time, but I haven't yet had an industrial control application that cried out for it loud enough to make me learn how to actually use it. One of these days, perhaps.

I have managed to apply a couple of Smith Predictors, which are sort of related but deal with delay in a control loop, instead of noise.

Another good site. I suspect you would end up teaching me very quickly.

(snip)

You might be able to learn very quickly, but I doubt I can teach quickly. ;-)

That's ok. stay in this NG, your expertise is very welcome.