Supplementary dataset for paper: "Approximate non-linear model predictive control with safety-augmented neural networks"

Supplementary dataset for paper Henrik Hose and Johannes Koehler and Melanie N. Zeilinger and Sebastian Trimpe "Approximate non-linear model predictive control with safety-augmented neural networks".

The code to use this dataset is publicly available at https://github.com/hshose/soeampc

The dataset contains training and testing data to train an NN controller for three standard benchmark systems, a stir tank reactor, a quadcopter, and a chain mass system.

For each system, there are initial conditions as comma separated value in the `x0.txt` file, the MPC input trajectory in the `U.txt` file and the corresponding predicted state sequence in the `X.txt` file. MPC parameters are provided for each system. The dataset was computed using acados for SQP solving.

The dataset also contains pretrained neural network approximations of the dataset.These are provided in the `pretrained_models.zip` file. The neural networks were trained with tensorflow.