MHB Linear Quadratic Gaussian (LQG) regulators

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Linear Quadratic Gaussian (LQG) regulators combine state feedback control with estimation techniques to manage systems affected by noise and uncertainty. They minimize a quadratic cost function, balancing control effort and state error, which is essential for optimal performance. Understanding LQG requires familiarity with both Linear Quadratic Regulator (LQR) principles and Kalman filtering for state estimation. Recommended resources for deeper insights include textbooks by Troutman and John Burns, which are well-regarded in the field. A thorough grasp of these concepts is crucial for effective application in control systems.
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How do LQG regulators work?

I have read the Matlab page about them, Wikipedia, and a few schools notes on them but it isn't either clear to me or they are not adequately explaining how they work. All see is that we want to control something giving a quadratic cost.

Is there are more robust way to explain this (greater depth or detail for a better understanding besides control and quadratic cost)?
 
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I can't say I know about LQR's as much as I should, but I do know that my graduate school was pretty big on them, and the standard book we used was Troutman. There's a discussion of it in there. I also notice that John Burns, a prof at VT known for this stuff, has a textbook which they're using for next year. Perhaps your school's library has these books. I should think the Burns book would be excellent. I always heard good things about Burns as a prof.
 
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