In this paper, a nonlinear adaptive neural network tracking control with a guaranteed H∞performance is proposed for a constrained robot manipulator with plant uncertainties. The neural network is used to learn the unknown dynamics by an adaptive algorithm. Moreover, a force sensor is built to measure the forces and torques between the experimental robot UArm II end-effector and the environment. Finally, results obtained from the implementation of the proposed controller in the manipulator UArm II, under a constrained movement, are presented.
Decision and Control, 2005 and 2005 European Control Conference. CDC-ECC '05. 44th IEEE Conference on
Date of Conference:
12-15 Dec. 2005
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