In the field of control theory, the state space description is a formal way to write the dynamic equations of a linear system. State Space Averaging is an early and well accepted way to derive the model of a switching converter, that employs the state space description of the converter network. In your travels, you may hear people talk about State Space Averaging or if you read papers it may be referenced. And I want you to know that what I've been teaching you in this class is in fact State Space Averaging, except without the formality of writing the circuit equations in vector and matrix form. State Space Averaging does have some additional advantages. It provides a formal way to write the equations correctly. Where we identify what are the independent sources, the state variables, and the other dependent, or alpha quantities of the network? And write them in a very specific matrix or vector form. And, what we know from state space averaging is that we, if we can write our converter equations in state space form, then we can plug the result directly into the state space averaging method. And always get a correct and valid model of our switching converter. I think that we don't have time in this nine-week class to spend the hour or two of lecture that we take to cover all of this. But, I do want you to know that it exists, and that what you have learned in this class is in fact amount the same thing. So, what I'm going to do is append the full set of slides to this video which covers an introduction to how to write state equations for a, an electric circuit. A derivation of the state space averaging method and a statement of the results. And then an example of applying State Space Averaging to drive the AC model of a flyback converter. So, for those of you who are interested, or have the time I'd suggest you could look through those slides.