MATLAB Answers

Using LSTM network in Nonlinear MPC design?

15 views (last 30 days)
Armin Norouzi
Armin Norouzi on 11 Jun 2021
Commented: Armin Norouzi on 17 Aug 2021
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.

Answers (1)

Niccolò Dal Santo
Niccolò Dal Santo on 30 Jul 2021
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:
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
  1 Comment
Armin Norouzi
Armin Norouzi on 17 Aug 2021
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.

Sign in to comment.

Community Treasure Hunt

Find the treasures in MATLAB Central and discover how the community can help you!

Start Hunting!