I almost can't believe I actually understood that. Though I would never notice the trick about using the fact that we know we are using a ZOH, so u(tau) is a constant. Also, now I appreciate how it simplifies the integral. If it was a FOH, I wouldn't know what to do. Well, I suppose it wouldn't be a linear difference equation, would it? Well, I guess you could say that it would be the average of u[k+1] and u[k], at least as an approximation?

I was looking for a course like this for so long! Thanks for sharing it, Professor!

THanks alot !

Thanks for your video, Really appreciate it. Clear explanation and still useful in 2024.

Thank you so much. Finally understood Luenbergers Observer <3

Nice

so clear, thanks

Nice lecture. Actually this is pretty standard review of Laplace transforms from differential equations but they usually don't teach that s is a complex number in undergraduate courses in the US.

Can this robot be purchased?

can you help me by telling me what i should learn to build this robot?,please

your works are great,thank your sir.

Great course! May I know the name of the textbook please?

how to use this concept for making a robot automatic using camera and sensors

sir actually by adjusting the parameters we can also recover the input signal using this models ARX?

awesome, that you very much.

Thank you so much for this video.

Dear professor, thank you very much for uploading this lectures! they have been very helpful for my course on linear dynamic systems.

I need your help in LMI

Half way through the lecture you abandoned your normal lecture style and just belt fed the information. Ruined the lecture.

Great lecture, what textbook is this based on ?

I dont have much time but I'll ttake my time to learn all of your lectures sir.

far much better than the NPTEL lectures🤘❤️ thanks sir💕

Thank you! professor.

Hello sir is there any way to make the other videos for this class public? I would love to learn more about this topic from you, thank you!

许老师好（不知是哪个xu，如有冒犯敬请原谅）我有一个疑问就是，如果说考虑离散系统，x（k+1）=Ax(k)+Bm(k)(m(k)的元素全是状态变量x里的元素的二次方)。这种情况下我还能用离散系统的李雅普诺夫方程吗？

Thank you sir, for this session. Dear sir i have one doubt and that is, when this is solved by using MATLAB code the sign of the elements like P12 and P21 is positive but we have these elements with negative sign when we are calculating by hand. So what is the reason behind this sir?? Can you please help in this regard?? The MATLAB code i am using is given below. Please check it sir if possible. clear all;close all;clc; A = [-1 1; -1 0]; Q = [1 0; 0 1]; P = lyap(A,Q)

dank

Please, deal with the model predictive control, preview control and gain scheduling if possible.

Thank you for the video. It is very clear if one knows RLS but too simple to new people in this area.

Thank you professor, the lecture is so far the best control course I took. It is very intuitive and uses lots of hands-on examples. Different point of views are given in certain control concepts making me understanding the big picture of modern control theory.

Thank you sir for the beautiful lecture. Very much thanks.

Thanks for making these lectures available for free, Prof. Greetings from Germany!

This is really great since the Professor also presents an intuitive explanation of why we use Lyapunov theorem, and the two-way proof is just really helpful for me to understand it. This explanation is crystal clear and thank you for the lecture.

I like your way of teaching, perfect!

Great job, Xu!

intuitive and clear, thx!

This is the best lecture! Is there further video on MPC?

Really good!!!!

Welcome to the class! Find lecture notes and additional material at the course website faculty.washington.edu/chx/teaching/me547/.

Really nice video!

So fabulous!