Motivation for Full-State Estimation [Control Bootcamp]

This video discusses the need for full-state estimation. In particular, if we want to use full-state feedback (e.g., LQR), but only have limited measurements of the system, it is necessary to estimate the full state.

These lectures follow Chapter 8 from:
"Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Brunton and Kutz

This video was produced at the University of Washington

[TomLawsonANIMATIONS]

I just graduated with an engineering degree, I know all the theory, I can do the maths with my eyes closed... But what I was always missing was a global intuition of the big picture in control. A lot of the theory seemed disconnected in my head and far from application. This series has been great for putting it all together and has increased my understanding immeasurably! Thank you.

[divyanshkhunteta]

I am doing my Masters in System and Control after 6 years of my Bachelor's Degree. And these videos are most helpful in revising basics and dig into the advanced stuff. Can't thank you more. Extremely helpful.

[murjoshua]

This series is a pure gold for me!
While finishing my computer science degree, but with high interest in robots systems, I totally needed this to grab the missing contents on control systems 💯

[niske]

I am super motivated on Full-State Estimation now, thank you!

[SohamChakraborty]

I am working on a project where we are developing a product. The product has to be cheap and therefore cannot have too many of those expensive sensors needed to control the system. When I was first told that these sensors are only for testing and cannot be used in the product, I was bewildered. Like how will I even control the system then. Now that I know the concept of estimators, it makes so much more sense.

[doanhiep]

You are doing a great job when turning complicated things into easy understanding. Thanks a lot!

[Drone.Robotics]

Thank you Sir....I have seen the whole series and it have cleared lot of my concepts about control theory. Your videos are just great and your way of teaching complex things in simple manner is appreciable. Thanks Again.

[elekzon]

Thank you so much Sir, you make this course much much easier to understand, simpler and intuitive.

[javierramon]

Thank you so much. Now I can see the whole picture. I don't know why in universities they don't start with such a great intro as yours. :)

[gabrielh]

Thanks for your videos, professor. I would like to know how this concepts scale into reference tracking.

[architektwo]

I laughed a whole minute about the pause before he realized he forgot the y. 10/10

[gayathrimenon]

Professor, I have a doubt regarding the LQR concept that is explained here. Why isn't there any reference signal to the system? So directly feeding back the controller gain to the system is sufficient?

[omeryugenkorat]

You write the equation for y based on x. Does it mean x after being updated with xdot? Because otherwise x would stay the same forever right?

[phamhuutri]

What does it mean to “ estimate state x from measurement y “ ? Should we know x already since the first equation x dot = Ax + Bu is the equation to calculate the next state ? Or is there something wrong with my understanding here ?

[ajj]

Sir, I love u and Ur misses work. I think your team is doing amazing work by making your teaching freely available! Truly thank you.
I am confused on how to input some thing to my system if the u=-kx. I have u which is a step input. Could you please clarify this for me.
i get it, you are feeding the states not the input. the input comes to the left of ure -ku with a summation block so like y-uk into the system

[AirAdventurer]

"Where were you when I was [28] [years old]?" "Getting a brace put on my teeth" - Rick Blaine & Ilsa Lund, _Casablanca_ (1942) (Not intended to be creepy - not sure if I should include this parenthetical comment)