期刊
IEEE TRANSACTIONS ON NEURAL NETWORKS
卷 21, 期 12, 页码 1857-1868出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNN.2010.2076302
关键词
Contact maintenance; force/position tracking; neuro-adaptive control; prescribed performance
类别
资金
- Alexander S. Onassis Public Benefit Foundation [G ZD 045/2007-2008]
In this paper, we address unresolved issues in robot force/position tracking including the concurrent satisfaction of contact maintenance, lack of overshoot, desired speed of response, as well as accuracy level. The control objective is satisfied under uncertainties in the force deformation model and disturbances acting at the joints. The unknown nonlinearities that arise owing to the uncertainties in the force deformation model are approximated by a neural network linear in the weights and it is proven that the neural network approximation holds for all time irrespective of the magnitude of the modeling error, the disturbances, and the controller gains. Thus, the controller gains are easily selected, and potentially large neural network approximation errors as well as disturbances can be tolerated. Simulation results on a 6-DOF robot confirm the theoretical findings.
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