An acceleration-based hybrid learning-adaptive controller for robot manipulators
Evren Han, Sanem and Ünel, Mustafa (2019) An acceleration-based hybrid learning-adaptive controller for robot manipulators. Transactions of the Institute of Measurement and Control (SI), 41 (8). pp. 2114-2123. ISSN 0142-3312 (Print) 1477-0369 (Online)
This is the latest version of this item.
Official URL: http://dx.doi.org/10.1177/0142331218780224
The robust periodic trajectory tracking problem is tackled by employing acceleration feedback in a hybrid learning-adaptive controller for n-rigid link robotic manipulators subject to parameter uncertainties and unknown periodic dynamics with a known period. Learning and adaptive feedforward terms are designed to compensate for periodic and aperiodic disturbances. The acceleration feedback is incorporated into both learning and adaptive controllers to provide higher stiffness to the system against unknown periodic disturbances and robustness to parameter uncertainties. A cascaded high gain observer is used to obtain reliable position, velocity and acceleration signals from noisy encoder measurements. A closed-loop stability proof is provided where it is shown that all system signals remain bounded and the proposed hybrid controller achieves global asymptotic position tracking. Results obtained from a high fidelity simulation model demonstrate the validity and effectiveness of the developed hybrid controller.
Available Versions of this Item
Repository Staff Only: item control page