Voltron Demo1: Autonomous Steering
2:16
Class 07: Katz Centrality
9:09
3 жыл бұрын
Class 01: Visualization
10:15
3 жыл бұрын
MATLAB sisotool Introduction
6:31
4 жыл бұрын
MATLAB rltool Introduction
12:41
4 жыл бұрын
Пікірлер
@prateekyadav9811
@prateekyadav9811 13 күн бұрын
This is was fantastic! Thank you!
@gaurabchan2171
@gaurabchan2171 2 ай бұрын
outstanding expression.
@matmanymat
@matmanymat 3 ай бұрын
thank you
@abdelrahmanwaelhelaly1871
@abdelrahmanwaelhelaly1871 3 ай бұрын
Thank you
@AkshayMohabeyAR
@AkshayMohabeyAR 6 ай бұрын
This is so great. Wish you could provide the slides notes for this 😊
@maxzim-dude
@maxzim-dude 8 ай бұрын
what are you really talking about? cuz didn't get what exactly an Erdos-Renyi graph is. the definition I mean
@ark1tech
@ark1tech 8 ай бұрын
what happened to the weight of the car?
@maverickreynolds
@maverickreynolds 9 ай бұрын
Thank you for this video!
@adrienguidat2214
@adrienguidat2214 11 ай бұрын
crystal clear, nice job
@mandlikprajwal466
@mandlikprajwal466 Жыл бұрын
Must have used some laser pointer or something to tell
@everythingrobotics
@everythingrobotics Жыл бұрын
If the system is non-linear (x_dot = f(x,u) ), is it a good approach to solve for [ f(x,u) ; r-cx] = [0 ; 0] using Newton Raphson or something and then linearize about that point to apply LQR based tracking?
@fabbritechnology
@fabbritechnology Жыл бұрын
These videos are great. Which paper did the Local Attachment model come from? Edit: I think I found it, "Local preferential attachment model for hierarchical networks", Wang et al.
@kirkpetersjr
@kirkpetersjr Жыл бұрын
This helped me, graduate ODE course
@LydellAaron
@LydellAaron Жыл бұрын
Lyapunov videos have been showing up in my feed. Thank you for your explanation.
@ayoubjibouni1428
@ayoubjibouni1428 Жыл бұрын
thank you the best explanation ever seen
@THEoneandonlystika
@THEoneandonlystika Жыл бұрын
useless
@sitiishrn_
@sitiishrn_ Жыл бұрын
haloo.. can you recommend a book to learn about basins of attraction? thanks🙏
@Let.s_Connect
@Let.s_Connect Жыл бұрын
Perfect
@aliseymenalkara4628
@aliseymenalkara4628 Жыл бұрын
You really, really explain things great... very understandable.
@aliseymenalkara4628
@aliseymenalkara4628 Жыл бұрын
the last part at the very end is not understood. the connection between state-space equation and h(t-tau)*u(tau) integral. H(s) stuff. Could you please elaborate on this connection?
@aliseymenalkara4628
@aliseymenalkara4628 Жыл бұрын
Why is D*u(t) = zero ?
@qvt5935
@qvt5935 Жыл бұрын
Thank you
@engenhologia
@engenhologia Жыл бұрын
Excellent! This was the linkage that I´ve needed
@engenhologia
@engenhologia Жыл бұрын
Amazing work! We have a similar project going on for many years now at IFSC (Federal Institute of Santa Catarina), but with so scarse resourses we've couldnt get it finished. What you are doing are benchmark to us.
@engenhologia
@engenhologia Жыл бұрын
Thank you, sir!
@DrMuzis
@DrMuzis Жыл бұрын
eyw
@gauffreb4529
@gauffreb4529 Жыл бұрын
I have a problem with your explanation at 4:40 ; If E(t) >= 0 and the derivate is negative, then it only means that E(t) converge to a positive number - not to 0 Then i think I missed a point somewhere ?
@Tony-mq5yo
@Tony-mq5yo Жыл бұрын
Very good!
@julieg1767
@julieg1767 Жыл бұрын
thank you sir
@muhammdaiyaz82701
@muhammdaiyaz82701 Жыл бұрын
dear professor i am working in double hopf bifurcation. I have concussion some points. first question is that any method or tool to check this any model hopf bifurcation exist or no? kindly reply me,. I also send a request in link din
@muhammdaiyaz82701
@muhammdaiyaz82701 Жыл бұрын
dear professor your method is outstanding
@PunmasterSTP
@PunmasterSTP Жыл бұрын
I was just reading about patterns in systems of reaction and diffusion and I decided I needed to learn more about bifurcations. This video was just the introduction that I needed!
@prateekyadav9811
@prateekyadav9811 Жыл бұрын
Thank you! This was excellent. Lucid and to the point. Much appreciated.
@ExoticVerse1
@ExoticVerse1 2 жыл бұрын
brilliant, thank you soo much and i appreciate your work
@kdSU30
@kdSU30 2 жыл бұрын
10:49 These paths tend to reach limit cycle, but only reach it at time equal to infinity right?
@andrezjoseph8888
@andrezjoseph8888 2 жыл бұрын
Good video, thank you.
@muskduh
@muskduh 2 жыл бұрын
Thanks for the review!
@107MrP
@107MrP 2 жыл бұрын
Thanks!
@helloworld1537
@helloworld1537 2 жыл бұрын
The content is good, but the volume is accidently too loud or too silent, which is not an experience for those who wear headphones.
@martinramirezjr7872
@martinramirezjr7872 2 жыл бұрын
You sir have done an absolutely excellent explanation of this process. Much clearer than in class. Thank you very much!
@Minji_Hanni_Dani_Haerin_Hyein
@Minji_Hanni_Dani_Haerin_Hyein 2 жыл бұрын
still watching even i don't fully understand English
@xaverpfk
@xaverpfk 2 жыл бұрын
Great explanation! Greetings from Germany
@gokhankiremit4322
@gokhankiremit4322 2 жыл бұрын
Thank you for great video !! I have a question, why tracking error, e = r - y instead of e = r - ( y+v) ?
@aldotoffano4670
@aldotoffano4670 Жыл бұрын
I had the same question. In a course that I am attending now at TAMU we did consider the second case and the result is indeed different
@stevencheung1120
@stevencheung1120 2 жыл бұрын
Thank you sir. I'm doing a Data Science course and the final vector form puzzled me. You explained well!
@rev0cdevs38
@rev0cdevs38 2 жыл бұрын
Super clear!
@arthurm7846
@arthurm7846 2 жыл бұрын
Great video, straight to the point and very detailed as well. Thanks Justin!
@sohamdhodapkar3212
@sohamdhodapkar3212 2 жыл бұрын
@jruths Super helpful video! Is it true that in a configuration model, in the large N-limit, all networks with given degree sequence are sampled approximately uniformly?
@moosa1037
@moosa1037 2 жыл бұрын
Thank you for this very informative video
@mustafasiddiqui8203
@mustafasiddiqui8203 2 жыл бұрын
Keep up the good work!
@SitDownMr
@SitDownMr 2 жыл бұрын
Hello thank you for the video, do you have the psuedocode for this model?
@ryantabor2593
@ryantabor2593 2 жыл бұрын
its a great video but i wish you could have worked through the simplification using the quotient rule just because you're making a massive jump which isn't very helpful to those watching.