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NN Predictive Controller for MIMO systems

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Abdulhamid Jalab
Abdulhamid Jalab il 27 Lug 2022
Modificato: Manikanta Aditya il 11 Gen 2024
Good morning
I'm trying to realize a NN predictive controller for a system (which I only have as a neural network) with more than one manipulated value for a project at the uni.
The Problem is, whenever I try to do the system identification with the data of my planet it gives me the error message "The input and output arrays mut be row (1*N) or column (N*1) variables".
And I'm stuck right there, so could someone help me?
Is there any way to implement the NN predictive controller for MIMO or MISO systems?
many thanks in advance
  6 Commenti
Sam Chak
Sam Chak il 2 Ago 2022
Modificato: Sam Chak il 2 Ago 2022
@Nohayla Ajmi, thanks for your reply. If your ultimate goal is to a design a control law, perhaps you can try to estimate the model using some techniques in the System Identification Toolbox.
bahouli hakima
bahouli hakima il 31 Ott 2022
Modificato: bahouli hakima il 31 Ott 2022
@Abdulhamid Jalab did you know how to implement the nn predictive controller with MIMO system, please if yes let me know how..

Accedi per commentare.

Risposte (1)

Manikanta Aditya
Manikanta Aditya il 11 Gen 2024
Modificato: Manikanta Aditya il 11 Gen 2024
Hi Abdulhamid,
Looks like you are trying to realize a NN predictive controller for a system with more than one manipulated value for a project. Then you are a error and also you would like if there is a way to implement the NN predictive controller for MIMO or MISO systems.
Regarding the error ‘The input and output arrays mut be row (1*N) or column (N*1) variables’, the issue seems to be with the dimensions of the input and output arrays. When working with matrices or arrays, you need to ensure that the dimensions match the expected format. You might need to transpose your array to match the expected format.
Model Predictive Control (MPC) is a method that can be used for controlling systems. MPC uses a model of the system to predict future behavior and optimize the process over a defined time period.
Please refer to the following reference to know more about
Hope it helps!

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