  Recently, a time-varying quadratic programming (QP) framework that describes
the tracking operations of redundant robot manipulators is introduced to handle
the kinematic resolutions of many robot control tasks. Based on the
generalization of such a time-varying QP framework, two schemes, i.e., the
Repetitive Motion Scheme and the Hybrid Torque Scheme, are proposed. However,
measurement noises are unavoidable when a redundant robot manipulator is
executing a tracking task. To solve this problem, a novel integration-enhanced
recurrent neural network (IE-RNN) is proposed in this paper. Associating with
the aforementioned two schemes, the tracking task can be accurately completed
by IE-RNN. Both theoretical analyses and simulations results prove that the
residual errors of IE-RNN can converge to zero under different kinds of
measurement noises. Moreover, practical experiments are elaborately made to
verify the excellent convergence and strong robustness properties of the
proposed IE-RNN.
