Neural Network Based Control System Design Toolkit Version 2
The NNCTRL toolkit is a set of tools for design and simulation of control systems based on neural networks. The following designs are available:
o Direct inverse control
o Internal model control
o Control by feedback linearization
o Optimal control
o Control using instantanous linearization (includes approximate pole placement, approximate minimum variance and approximate GPC control)
o Nonlinear Predictive Control
o Nonlinear Feedforward Control
The toolkit is an add-on to the NNSYSID toolbox; a toolbox for nonlinear system identification with neural networks. This version of the NNCTRL toolkit requires MATLAB 5.3 or higher. It is an advantage if Simulink and the Control System Design Toolbox is available (but they are not required). A manual (~35 pages, pdf format) accompanies the toolbox. Additional information can be found on www.iau.dtu.dk/research/control/nnctrl.html
Magnus Norgaard (2021). nnctrl (https://www.mathworks.com/matlabcentral/fileexchange/86-nnctrl), MATLAB Central File Exchange. Retrieved .
In the function apccon.m
In theory it says, to get a i step ahead prediction we need to solve the DIOPHANTINE equation for both E and F. In the function provided, we have initialized both the variable but later in the calculation we are not using 'E' at all. We are calculating everything with 'F'. Can you please provide any literature which explains the method you have implemented in the program?
Thank you for providing the whole framework. It is been really help in my thesis now.
Thank you for your valuable toolbox. I was intrigued by its applicability in a wide range of well-known control strategies.
Excellent program! Your work is an outstanding contribution. Nowadays they talk about Machine Learning Control as it is something "new" while decades ago people already investigated the field... I think it is quite common with everything "new" i guess :)
thank you for your efforts
this files very imprtant
I AM TOO SORRY BUT POOR
i agree with that, you need to put comments on codes, cause there are a lot of codes inside the codes..
it seems to be that it need improvement
Hard To Understand (Because there are a lot of subcodes), Need Comments
Good for study and extension into real problem. I like it and now study it, since I want to use it to control my problem. Does anybody like to give me suggestion about IMC? thanks
it is very useful toolbox for neural network
please send nonlinear internal model control using fuzzy based inverse model
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