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Control theory is a super fascinating field.

by gaze 11y ago

tantalor 11y ago
Care to elaborate? What do you find fascinating about it.
- gumby 11y ago
  it's chock-a-block with counterintuitive behavior, problems,approaches and solutions.
- Animats 11y ago
  Much of modern control theory is equivalent to machine learning. "Adaptive feedforward control" is a form of machine learning. So is "automatic system identification". Back in the 1990s, before machine learning had really got going, I thought that adaptive feedforward control was a path to AI. In a sense it was, but it took a lot of mathematical development in other fields to get it there. Feedforward control is unusual. In feedback control, you look at the error and try to zero it out. Usually, there's some lag in the system, which leads to undershoot, overshoot, oscillation, and all the usual problems of feedback systems. Maxwell (yes, that Maxwell) figured all this out in 1868, and his little paper "On Governors" defined linear control theory for a century.[1] Feedforward control, though, is about measuring disturbance inputs, predicting what's going to happen, and adjusting controllable inputs so that the error remains near zero. An example is a heating system with an input for outside temperature as well as inside temperature. When the outside gets cold, the heating plant will be cranked up in advance of the inside getting cold. This is essential if the system being controlled has a lot of lag, like a big building's heating system. Adaptive feedforward control is automatically identifying how the inputs affect the outputs by watching everything for a while. It's thus a form of machine learning. For the heating system example, a good controller might learn that a 10 degree drop outside means a 5 degree drop inside 1 hour later. It might also learn that the heating hot water temperature rises at 30 degrees an hour when the boiler is at full power. Such info can be used to decide to crank up the boiler half an hour after the outside temperature drops, so heating water will be available just when it's needed. This is a basic form of machine learning. One big difference from the machine learning community is that controls people demand error bounds on their control algorithms. So the machine learning techniques used tend to be the ones ameninable to error analysis. Support vector machines, yes; deep neural nets, not so much. [1] https://commons.wikimedia.org/wiki/File%3AOn_Governors.pdf https://commons.wikimedia.org/wiki/File%3AOn_Governors.pdf
- planteen 11y ago
  Kalman filtering is very interesting. You are able to combine a bunch of inputs, of varying levels of uncertainty, along with a model of the system to arrive at an optimal estimation of the state.
- sheepleherd 11y ago
  the math is amazing. Convolution integral, Laplace transform, Z transform, frequency domain, impulse response, step response, digital filters... it's all amazing.

Control theory is a super fascinating field.

by gaze 11y ago

Control theory is a super fascinating field.

tantalor 11y ago

Care to elaborate? What do you find fascinating about it.

gumby 11y ago
it's chock-a-block with counterintuitive behavior, problems,approaches and solutions.
Animats 11y ago
Much of modern control theory is equivalent to machine learning. "Adaptive feedforward control" is a form of machine learning. So is "automatic system identification". Back in the 1990s, before machine learning had really got going, I thought that adaptive feedforward control was a path to AI. In a sense it was, but it took a lot of mathematical development in other fields to get it there. Feedforward control is unusual. In feedback control, you look at the error and try to zero it out. Usually, there's some lag in the system, which leads to undershoot, overshoot, oscillation, and all the usual problems of feedback systems. Maxwell (yes, that Maxwell) figured all this out in 1868, and his little paper "On Governors" defined linear control theory for a century.[1] Feedforward control, though, is about measuring disturbance inputs, predicting what's going to happen, and adjusting controllable inputs so that the error remains near zero. An example is a heating system with an input for outside temperature as well as inside temperature. When the outside gets cold, the heating plant will be cranked up in advance of the inside getting cold. This is essential if the system being controlled has a lot of lag, like a big building's heating system. Adaptive feedforward control is automatically identifying how the inputs affect the outputs by watching everything for a while. It's thus a form of machine learning. For the heating system example, a good controller might learn that a 10 degree drop outside means a 5 degree drop inside 1 hour later. It might also learn that the heating hot water temperature rises at 30 degrees an hour when the boiler is at full power. Such info can be used to decide to crank up the boiler half an hour after the outside temperature drops, so heating water will be available just when it's needed. This is a basic form of machine learning. One big difference from the machine learning community is that controls people demand error bounds on their control algorithms. So the machine learning techniques used tend to be the ones ameninable to error analysis. Support vector machines, yes; deep neural nets, not so much. [1] https://commons.wikimedia.org/wiki/File%3AOn_Governors.pdf https://commons.wikimedia.org/wiki/File%3AOn_Governors.pdf
planteen 11y ago
Kalman filtering is very interesting. You are able to combine a bunch of inputs, of varying levels of uncertainty, along with a model of the system to arrive at an optimal estimation of the state.
sheepleherd 11y ago
the math is amazing. Convolution integral, Laplace transform, Z transform, frequency domain, impulse response, step response, digital filters... it's all amazing.