DeePC-HUNT

Data enabled predictive control hyperparameter tuning via differentiable optimization layers

DeePC-HUNT is a method for optimising over the hyperparameters of DeePC, using anlaytical policy gradients. This method has been developed as part of my bachelor thesis, carried out at the automatic control lab. Supervised by Alberto Padoan, Keith Moffat and Florian Dorfler.

Developed in a conda environment on Ubuntu 22.04 with python 3.10.

Differentiable DeePC layer is inspired by Differentiable MPC and built using CvxpyLayers.

Installation

install via pip

pip install deepc_hunt

Or clone the repo and install via

cd DeePC-HUNT && pip install -e .

All extra packages necessary for running the example notebookds are in examples/requirements.txt. If needed,

pip install -r examples/requirements.txt

DeePC-HUNT has the following dependancies.

• Python3
• PyTorch >= 1.0
• CvxpyLayers >= 1.0

Usage

Data-​enabled Predictive Control (DeePC) is a data-​driven non-​parametric algorithm for combined identification (learning) and control of dynamical systems. It leverages on the solution of the following optimization problem in a receding horizon fashion

DeePC can achieve performance that rivals MPC on non-linear and stochastic systems (see here), but is highly sensitive to the choice of regularization parameters $\theta_i$. DeePC-HUNT addresses this problem by automatically tuning these parameters. The performance of DeePC-HUNT has been validated on a noisy cartpole and LTI system. These examples can be viewed in the notebooks in the examples directory.

Cartpole - before training

https://github.com/michael-cummins/DeePC-HUNT/assets/72135336/1c73ca21-91c1-4669-a04d-1d9a103d9d48

Cartpole - after training (episode 70)

https://github.com/michael-cummins/DeePC-HUNT/assets/72135336/b6fa9948-a768-4646-b87f-018395fcc6c4