Using LSTM network in Nonlinear MPC design?

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Armin Norouzi
Armin Norouzi 2021-6-11
评论： MD RAHAT 2023-9-14
Hello everyone,
I would like to identify a system that has three inputs [u_1(k) u_2(k) y(k-1)] and single output as y(k) using LSTM time series estimation. I have a couple of questions regarding the implementation of this model in nonlinear MPC.
Based on the documentation of NMPC, I need to define a function for a state called StateFcn and an output called OutputFcn. As my model is based on the LSTM network, I was wondering how I can do that? Unfortunately, I couldn't find any example when I dig more into it. It worth mentioning that I am using GT-suit co-simulation as a virtual test machine, and I am going to implement this LSTM-based MPC to that.
Thank you in advance for your help.
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Dun-Ren Liu
Dun-Ren Liu 2022-12-26
I face the same problem.can you please share the solution?
thanks~
MD RAHAT
MD RAHAT 2023-9-14
I am having exactly same problem. Can you please guide me a little if you have found the solution to it

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回答（1 个）

Niccolò Dal Santo
Niccolò Dal Santo 2021-7-30
Hi Armin,
If I understand correctly you'd want train an LSTM for a time series with feedback. You can follow this example which shows how do that:
https://www.mathworks.com/help/deeplearning/ug/time-series-forecasting-using-deep-learning.html?lang=en
You should define your inputs as a three-elements vector ([u_1(k) u_2(k) y(k)], hence numFeatures = 3), one response and train your LSTM accordingly.
For further reading, here is an example for training an LSTM with more than one input feature: https://www.mathworks.com/help/deeplearning/ug/sequence-to-sequence-regression-using-deep-learning.html
Hope this helps.
Cheers,
Niccolò
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Armin Norouzi
Armin Norouzi 2021-8-17
Thank you for your response. I modeled my system using LSTM, and my main problem is how to use this model inside nlmpc mode. I understand that this model predicts sequence output for given sequence input. However, in nlmpc, I need to provide x(k+1) = f(u(k)) model, i.e., for given inputs in the previous time step, a model needs to be capable of estimating the next time step output. I would appreciate it if you could share your thoughts about this matter.

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