Enhanced Adaptive Control over Robotic Systems via Generalized Momentum Dynamic Extensions
Received 22 November 2023; accepted 15 December 2023; published 11 January 2024
2023, Vol. 19, no. 4, pp. 633-646
Author(s): Nedelchev S., Kozlov L., Khusainov R. R., Gaponov I.
Adaptive control and parameter estimation have been widely employed in robotics to deal
with parametric uncertainty. However, these techniques may suffer from parameter drift, dependence
on acceleration estimates and conservative requirements for system excitation. To overcome
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 accelerationfree
momentum dynamics and regressor extensions to offer faster parameter and tracking convergence
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.
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