DeePC-HUNT is a method for optimising over the hyperparameters of DeePC using analytical policy gradients and differentiable optimization layers. This method has been developed as part of my bachelor thesis, carried out at the Automatic Control Laboratory (IfA). 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.
Clone the repo and install from source
cd DeePC-HUNT && pip install -e .
Extra packages necessary for running the example notebooks are in examples/requirements.txt if needed.
pip install -r examples/requirements.txt
DeePC-HUNT has the following dependencies.
- Python3
- PyTorch >= 1.0
- CvxpyLayers >= 1.0
Data-enabled Predictive Control (DeePC) is a data-driven non-parametric algorithm for combined identification (learning) and control of dynamical systems. It leverages 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
Untrained-episode-0.mp4
After running DeePC-HUNT for 70 episodes, the controller now stabilizes the system.