In this paper, a neural network-based adaptive iterative learning control scheme is developed to solve the trajectory tracking problem for rigid robot manipulators with arbitrary initial errors. Time-varying boundary layers are used to relax the zero initial error condition which must be observed in traditional iterative learning control design, and adaptive learning neural networks are constructed to approximate uncertainties in robotic systems, whose optimal weights are estimated by using partial saturation difference learning method. For arbitrary bounded initial state errors, the tracking error of robot manipulators will asymptotically converge to a tunable residual set as the iteration number increases. An illustrative example and the comparisons are provided to demonstrate the effectiveness of the proposed neural network-based adaptive iterative learning control scheme.INDEX TERMS Iterative learning control, neural networks, robot manipulators, adaptive learning control.

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Xiaohong Dong was born in Hebei, China. She is currently pursuing her PhD degree at Tianjin University. Her research interests are electric vehicle charging infrastructure planning and power system stability analysis.

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