Supplementary dataset for paper: "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling"

Supplementary datasets and pretrained models for the paper "Towards Safe Learning-Based Non-Linear Model Predictive Control through Recurrent Neural Network Modeling" (Clement et al., 2026). This record contains the data and models used in the Sequential-AMPC framework available at the GitHub repository seq-ampc(1) vehicle kinematic-obstacle and vehicle dynamic-obstacle datasets generated for this work; and(2) pretrained neural network models used in the experiments. The original SOEAMPC supplementary dataset is available at DOI: 10.5281/zenodo.7846094 from which the quadcopter dataset was used. The code used to generate, train, and evaluate the models in this record is available at the associated GitHub repository for Sequential-AMPC. File organization: vehicle_obs_N_55000.tar.lz: kinematic bicycle model with static obstacle avoidance vehicle_8state_obs_N_116000.tar.lz: dynamic single-track vehicle model with static obstacle avoidance models.zip: pretrained models for the experiments in this work For each dataset, files contain initial conditions, MPC input trajectories, predicted state sequences, and associated parameters as documented in the repository README.