Thanks for your feedback!
With regards to your question, I had addressed that topic in my previous post. Briefly put, an LQG controller is an estimator based-controller where the regulator is an optimal controller and the estimator is a Kalman filter (hence the name, Kalman estimator). The Symbol KalmanEstimator[] gives you the estimator gains for the Kalman estimator, similar to how in this example, EstimatorGains was used to calculate the gains of the estimator for the estimator-regulator with the difference being that you use estimator gains when the information regarding your states is incomplete (unobservable), while the Kalman estimator also takes in to account the noise to from your sensors. This all comes down to the conclusion that if you have your process and noise covariance inputs, then you can obtain an LQG directly using the symbol LQGRegulatorGains!
