Modeling: Classification of State Space Representations (Lectures on Advanced Control Systems)

State space representation is a powerful tool for modeling and designing control systems, and can be used for a wide range of applications. In this video, we cover classification of these representations. Have fun!

[mohammadeqbalbalaghi]

great job you are doing here by teaching these stuffs intuitively

[Crusader_No_Regret]

Congratulations teacher, another excellent class! Thanks.

[lyapunov]

1. In the video "Gain-Scheduled Control" you use a convex function depending on the scheduling variable alpha to blend the linear operating point controllers. (u = u1(1-alpha) + u2*alpha). Can you perhaps make a video in which you go into detail about what interpolation methods there are for linear operating point controllers to control nonlinear systems?
2. I am also interested in the topic of LMI's and how they can be used for stability analysis of gain-scheduled control systems.
3. Are you also inerrested in "model predictive control" and maybe you like to make videos to thate topic too?

You have the best control systems channel on the internet. Thanks a lot for sharing your knowledge. It is a huge pleasure to watch your videos. I hope we will see much more from you. :)

[zuhair]

OMG, you are great Sir. !
Thanks for every detail you have provided in this video. I like you intuitions 😁 for applied mathematics.



Yes please if it is possible to make a related lecture of non-holonomic vs holonomic systems. 🙏

[hoytvolker]

In the first example, why did not we choose the mass as a state although it has a derivative ? It seemed like a non linear time invariant state space model

[tonnirvana]

Thnx for the great list, Do you still consider to add feedback linearization to the list?