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Fine tuning a PID controller

It is very rare to see a discussion of PID controllers without also learning about how important it is to properly tune the controller. Much of the literature talks about empirical tuning of the controller. The PID equation is based upon an integro-differential equation and the ``gain'' factors are determined by a combination of the desired gain plus the effect due to numerically approximating the different parts of the equation. Part of the preoccupation with empirical tuning PID controllers is cultural and part of it is practical. I have noticed that it is a rare engineer (as opposed to scientists) that actually spends the time to analytically solve differential equations, and not many scientists or engineers have the skills to solve integro-differential equations in their bag of tricks. Fortunately the equations are linear, which means that they are solvable with such techniques as Laplace transforms. A second reason why PID controllers need to be empirically tuned is that often for real-world systems the exact equations aren't really known, especially given the uncertainties inherent in a system containing mechanical devices and noise prone sensors.

It is also possible to design self-tuning controllers, by adding an adaptive tuning section to the control software. We will leave the topic of adaptive systems for another time.


next up previous
Next: Experimenting with the sample Up: number7 Previous: The PID controller
Skip Carter 2008-08-20