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@mayataka Thank you for much for helping and kindness. I will to understand your notebook as soon as possible so that we can discuss about opc ;)!
片山さん @mayataka
東大工学部機械工学科3年の上條と申します。
分かり易い資料を公開していただきありがとうございます。大変勉強になりました。
5. モデル予測制御CasADiによる実装のMPCクラスを定義している部分で、am_g0 = [] # 制約 lbg以下がこのページでは表示されていないようです。些細なことですが、念の為報告させていただきます。
@tatsukamijo
ごいただき報告ありがとうございます．本当ですね．ダウンロードすれば問題ないようですね．
@mayataka Hi Mayataka, I have study some of your notebooks of AutoGenU. But I really don't understand Where is the closed loop of the system is happend? since I am planning to implement it in real-time robot with C/GMRES method on NMPC. Can you help indicate me where is it ? Thank you in advance!
@SokhengDin
Could you write the issue in https://github.com/mayataka/autogenu-jupyter? This is not the place to discuss autogenu-jupyter.
Hello， Your paper is very good. "A moving switching sequence approach for nonlinear model predictive control of switched systems with state-dependent switches and state jumps". I'm very interested in your work. I would like to ask you some questions. 1."If the instant of the first switch of the switching sequence after the solution is updated is less than
the next sampling time, we predict the actual switch between the current sampling time and the next sampling time
and then remove variables related to the switch from the solution". How to implement in the program？ Is it convenient to provide sample programs for learning？I'm looking forward to your reply. I wish you a happy day. Thank you very much.@mayataka
Here I'm willing to answer questions regarding MPC and optimal control. But your question is totally out of the scope of this notebook.
I hope you can find a good place to ask your questions.