Enhanced Adaptive Control over Robotic Systems via Generalized Momentum Dynamic Extensions

Adaptive control and parameter estimation have been widely employed in robotics to deal with parametric uncertainty. However, these techniques may suffer from parameter drift, depen- dence on acceleration estimates and conservative requirements for system excitation. To over- come these limitations, composite adaptation laws can be used. In this paper, we propose an enhanced composite adaptive control approach for robotic systems that exploits the acceleration- free momentum dynamics and regressor extensions to offer faster parameter and tracking con- vergence while relaxing excitation conditions and providing a clear physical interpretation. The effectiveness of the proposed approach is validated through experimental evaluation on a 3-DoF robotic leg.