Neural-Augmented Torque Control for Robotic Manipulators: Modeling, Learning, and Real-Time Performance
This thesis investigates advanced modeling and control strategies for robotic manipulators, focusing on the DLR-HIT II robotic hand and the KUKA LBR iiwa. It presents three core contributions that integrate simulation, model-based control, and data-driven methods to improve torque and position control under uncertainti...