Intermittent Iterative Learning Control for Robot Manipulators Under Packet Dropouts

This paper is concerned with the intermittent iterative learning control for robot manipulators with packet dropouts. A composite controller is presented by a proportional-derivative feedback and an iterative learning feedforward to deal with the dropouts. A modified reference trajectory is adopted to treat input intermittence. With the help of composite energy function, rigorous theoretical derivations are provided to ensure the convergence of the estimation error of the iterative estimator and the boundedness of the tracking error. The validity of the proposed method is demonstrated by simulation studies on a single-link manipulator and a two-degrees-of-freedom planar manipulator, respectively. Note to Practitioners—Robot manipulators have drawn significant attentions due to their wide industrial applications. Due to their repetitive feature, iterative learning control is an effective strategy to achieve high tracking accuracy for them. However, this strategy faces new challenges incurred from network environment, such as packet dropouts and how to save energy. To patch this gap, an interpolation method is exploited for an iterative estimator to handle packet dropouts and a modified reference trajectory is employed to tackle with input intermittence. Simulation studies with practical background is supplied to show the validity of the proposed method. Therefore, this paper lays a theoretical basis for the tracking control of robot manipulators in practical applications.