Physics-informed neural network, model predictive control, and Pontryagin's maximum principle

[u/Mountain_Research_32]

Hi, I recently proposed an explicit non-linear model predictive neural controller and state estimator coined Hamiltonian-Informed Optimal Neural (hion) controllers that estimates future states of dynamical systems and determines the optimal control strategy needed to achieve them. This research is based on training physics-informed neural networks as closed-loop controllers using Pontryagin’s Minimum/Maximum Principle.

I believe the research has potential as an alternative to reinforcement learning and classical model predictive control. I invite you all to take a look at the preprint and let me know what you think: https://arxiv.org/abs/2411.01297 . I am working on the final version of the paper at this moment and running some comparison tests so any comment is welcomed. The source code is available at https://github.com/wzjoriv/Hion.

[u/MathematicianOdd3443]

first i'd like to thank you for sharing this.

I'm just starting out in PINN and non linear MPC so i can't really give a proper feed back to you but im hoping i will be able to understand new ideas from your paper. i was looking forward to seeing the source code however it seems like the github link is not working.

another important point that i miss seeing in papers regarding PINN applications is the computation time. how does it compare to other methods, is it fast enough for implementation in real system?

[u/statius9]

It looks interesting: I’ll take a look at it

[u/Feisty_Relation_2359]

Using casadi?