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pnnl / neuromancer

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.
https://pnnl.github.io/neuromancer/
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NODE Control Adaptation #207

Closed DJIDM closed 1 week ago

DJIDM commented 1 week ago

Hello,

I am trying to adapt your tutorial Part 4: Learning neural ODE model and control policy for an unknown dynamical system. to my own application. My code is essentially the same as that in the Jupyter notebook; the only differences being that I import my own data instead of generating it, and I had to use my own custom normalize/de-normalize functions.

I am able to get good NODE system model identification: image

However, my control policy is not as performant: image

I am not sure what I am doing wrong and would appreciate any guidance. I've attached my notebook and data for your reference. NODE Control Adaptation.zip

drgona commented 1 week ago

Hi @DJIDM. Your system ID looks spot on. Your control policy also does what it is supposed to do. The reason it is not tracking the reference perfectly is that the dynamics is slower than the change in the reference. I suggest increasing the period of change of the desired reference to match the system dynamics and you should see better tracking.

Also this is not an issue with the library. I suggest moving this to the discussion channel.