I have mixed feelings about control theory (education). On one hand, the maths are fun, on the other hand:
0 - For simple things PIDs work well, but then you don't need an entire course on Control Theory to learn how PIDs work.
1 - Most books focus too much on maths an very little on applications. You'll see the same toy examples repeated
over and over in literature.
2 - For more complex things related to CT that's even worse. I've read some Multi-variable Control Theory books that were 100% maths, 0% on how to apply the goddamn thing you're learning.
3 - Complex CT techniques are often fragile (because you're modeling systems with high order polynomials), so most people just skip to using Machine Learning.
4 - You often need to make too many assumptions about a system to apply CT techniques.
I've actually used CT for a few things related to electronics circuits in my life, but overall I think most courses I took in university (Analog CT and Digital CT, Multivariate and Adaptive CT) were complete overkill and way too much theory without practical insights.
> 0 - For simple things PIDs work well, but then you don't need an entire course on Control Theory to learn how PIDs work.
Kalman filters seem pretty useful, especially with phones and stuff having an array of different sensors that can be combined, and probably require a class more than something more simple like a PID controller.
I used to be a control systems engineer in a previous life, working with ultra high precision position control systems, where PID just does not cut it.
Best approach I found was high-sample-rate LQE / LQR control - the LQE part is a Kalman filter, which is a piece of cake now, but was very hard to achieve with available hardware 25 years ago.
The biggest enemy of simplicity was nonlinearities, and much of the 'art' of precision digital control system designs was in knowing how to linearize them through software tricks and/or adapt the algorithm to provide piecewise linear control.
I worked on but didn't design the control algorithm for a zone controller where each of the five zones interacted with each other because they were part of a six foot tall quartz tube. There was compensation for the heating elements being nonlinear. And because the thermal mass was basically nothing radiation losses dominated it's behavior. Some of the control variables were squared then scaled. And zero overshoot was allowed for the hottest zone. And because it was a batch process it had to heat up quick.
I had to re-tune one after they replaced the quartz chamber.
0 - For simple things PIDs work well, but then you don't need an entire course on Control Theory to learn how PIDs work.
1 - Most books focus too much on maths an very little on applications. You'll see the same toy examples repeated over and over in literature.
2 - For more complex things related to CT that's even worse. I've read some Multi-variable Control Theory books that were 100% maths, 0% on how to apply the goddamn thing you're learning.
3 - Complex CT techniques are often fragile (because you're modeling systems with high order polynomials), so most people just skip to using Machine Learning.
4 - You often need to make too many assumptions about a system to apply CT techniques.
I've actually used CT for a few things related to electronics circuits in my life, but overall I think most courses I took in university (Analog CT and Digital CT, Multivariate and Adaptive CT) were complete overkill and way too much theory without practical insights.