Nonlinear Control: Hamilton Jacobi Bellman (HJB) and Dynamic Programming

Very good explanation to derivative of HJB equation. But there's a point I may have to add that I think there may be a typo in 'DERIVING HJB EQUATION': In dV/dt, minimizing the integral of L(x,u), the lower limits of integral should be t instead of 0. Only by the case, we can conclude in the second last equation that -L(x(t), u(t)) can be obtained from the time derivative of integral of function L(x,u)...

This is the first time I've ever seen the explanation of HJB-DP in a intuitive and fashionable way, not by following the text book lines one by one. Thank you so much for the great talk.

Excellent. Can see a lot of connections with Control and how the essence of Bellman equation are all over the place in different fields. Thanks Prof. Brunton!

Can't believe serious topic as this can have thousands of views hours after release. Youtube is really a magic place.

I am a follower from his 'control bootcamp' series. Just trying to tell everyone new here that his video is life-saving.

Wow it's so cool that these concepts from reinforcement learning apply so perfectly to nonlinear control.

One of the best lectures that I've ever seen!

thanks Steve for a great lecture; looking forward to more lectures on RL and non-linear control if possible with some simple examples. thank you very much!

Clear tutorial. Thanks Prof. Steve. Keep following your steps.

Hey Steve, on 9:11 it should be integration from t to t_f, then that’s where the - comes from.

A Great Lecture. I hope the next lecture will open asap. In particular, I'm interest in detailed relationship between RL and optimal control.

More on non linear control please! Im trying to make up my mind on topics for my postgrad thesis!

Steve I follow all of your lectures. Being a mechanical engineer I really got amazed by watching your turbulence lectures. I personally worked with CFD using scientific python and visualization and computation using python and published a couple of research articles. I'm very eager to work under your guidance in the field of CFD and Fluid dynamics using Machine learning specifically simulation and modelling of turbulence fluid flow field and explore the mysterious world of turbulence. How should I reach you for further communication?

Thanks Professor Steve, Finally I completed the playlist.

Please do more of this content. Thank you.

At 11:47 the bounds of the integral should be from “t” to “tf”; not from 0 to tf. If you make that change then the derivative of the integral wrt to t will be -L(.,.)

Great as always Steve! I was wondering if you have any experience in transfer learning, specifically domain adaptation? If so it would be a cool topic to go through! /J

Thanks dear steve for this wonderful tutorial I was wondering would it be ok if you solving an example for that?

Very nice video. In deriving the HJB equation, the lower limit of the integral should be t instead of 0.

This Hilbert space is include in f(x(k),u(k) * (x(0),y(k)-0) or outside the x(k) - (without double equation)?