I cannot wait to watch your videos! This is so nice! I am a mechanical engineer in germany that somehow got into IT during corona but I have a great interest in control theory and dynamical systems ever since my studies. Your content is pure gold! Thank you for your effort to teach us all these topics!
Please teacher, can you consider making a video of the MDPP adaptive controller with RLS? I thank. And taking advantage of the occasion, congratulations on the content, your classes are excellent!
Thank you for great lecture! In MRAC (Model Reference Adaptive Control), we do not have knowledge of the real model, so we cannot determine the parameter "B." Instead, we have knowledge of the reference model's parameters, "am" and "bm." However, in your example, it seems to be the opposite. I would greatly appreciate it if you could provide some clarification on this matter. Thank you.
sorry professor, in this video and in the playlist Adaptive Control we are considering model uncertainties such that the uncertainty f(x) appears with u(t) as B(u + f(x)). What about if we have disturbances such that \dot x = Ax + Bu + f(x)? Could you make a video about adaptation to disturbances rather than model uncertainties? Thank you
Can you do a video on multivariable MRAC state-feedback for output tracking. I am doing a related research for UAV control but it is a bit complicated. I am using Gang Tao papers as my reference.
This current video is on state-feedback MRAC. For a much more detailed version of it, please watch th-cam.com/video/c9VwaSEo5t8/w-d-xo.html. Now, to do output tracking, you need to choose your reference model. To design your reference model, you can use a feedback/feedforward approach or an integral approach. In this regard, I'd recommend that you watch first: th-cam.com/video/EWeFxseU6g4/w-d-xo.html
I cannot wait to watch your videos!
This is so nice!
I am a mechanical engineer in germany that somehow got into IT during corona but I have a great interest in control theory and dynamical systems ever since my studies.
Your content is pure gold! Thank you for your effort to teach us all these topics!
Thank you very much for your kind and motivating feedback 🙏 I am glad you liked the content 🤘
Thank you very much for your videos dear professor. Please how did you select or choose your basis function "beta(x)"
I am posting a new video tomorrow about your question. Stay tuned ;)
@@tyucelen Thank you Sir. I will be happy learn more on that.
Please teacher, can you consider making a video of the MDPP adaptive controller with RLS? I thank. And taking advantage of the occasion, congratulations on the content, your classes are excellent!
Thanks 🙏 Noted.
Thank you for great lecture!
In MRAC (Model Reference Adaptive Control), we do not have knowledge of the real model, so we cannot determine the parameter "B." Instead, we have knowledge of the reference model's parameters, "am" and "bm." However, in your example, it seems to be the opposite. I would greatly appreciate it if you could provide some clarification on this matter. Thank you.
sorry professor,
in this video and in the playlist Adaptive Control we are considering model uncertainties such that the uncertainty f(x) appears with u(t) as B(u + f(x)). What about if we have disturbances such that \dot x = Ax + Bu + f(x)?
Could you make a video about adaptation to disturbances rather than model uncertainties? Thank you
Can you do a video on multivariable MRAC state-feedback for output tracking. I am doing a related research for UAV control but it is a bit complicated. I am using Gang Tao papers as my reference.
This current video is on state-feedback MRAC. For a much more detailed version of it, please watch th-cam.com/video/c9VwaSEo5t8/w-d-xo.html. Now, to do output tracking, you need to choose your reference model. To design your reference model, you can use a feedback/feedforward approach or an integral approach. In this regard, I'd recommend that you watch first: th-cam.com/video/EWeFxseU6g4/w-d-xo.html