3 citations · 3 across the 1 of their papers we have counts for
4 papers
ExAMPC: the Data-Driven Explainable and Approximate NMPC with Physical Insights
Jean Pierre Allamaa, Panagiotis Patrinos, Tong Duy Son
Amidst the surge in the use of Artificial Intelligence (AI) for control purposes, classical and model-based control methods maintain their popularity due to their transparency and…
Learning Based NMPC Adaptation for Autonomous Driving using Parallelized Digital Twin
Jean Pierre Allamaa, Panagiotis Patrinos, Herman Van der Auweraer +1
In this work, we focus on the challenge of transferring an autonomous driving controller from simulation to the real world (i.e. Sim2Real). We propose a data-efficient method for o…
Real-time MPC with Control Barrier Functions for Autonomous Driving using Safety Enhanced Collocation
Jean Pierre Allamaa, Panagiotis Patrinos, Toshiyuki Ohtsuka +1
The autonomous driving industry is continuously dealing with safety-critical scenarios, and nonlinear model predictive control (NMPC) is a powerful control strategy for handling su…
Reinforcement Learning from Simulation to Real World Autonomous Driving using Digital Twin
Kevin Voogd, Jean Pierre Allamaa, Javier Alonso-Mora +1
Reinforcement learning (RL) is a promising solution for autonomous vehicles to deal with complex and uncertain traffic environments. The RL training process is however expensive, u…