Adaptive Iterative Learning Trajectory Tracking Control for Spraying Manipulator With Arbitrary Initial States and Iteration-varying Reference Trajectory

Abstract

This paper presents a novel adaptive iterative learning control (AILC) scheme to improve the tracking performance of the manipulator for spraying hull to achieve high-quality spraying effectively. First, a novel way of modifying the reference trajectory is proposed to deal with arbitrary initial states and iteration-varying reference trajectory when the manipulator reciprocates spraying. The modified way comprises constructing an error variable containing an initial correction term and determining the shortest limited time of completely tracking the desired trajectory by optimization principle. Based on this, an AILC scheme uses adaptive and backstepping techniques to handle the system's uncertain physical parameters and external disturbance. Theoretically, this control scheme can guarantee the tip-position of the spraying manipulator to perfectly track the desired reference trajectory within an appropriate, limited time under arbitrary initial states and iteration-varying reference trajectory. Simulations and experiments verify the proposed method's effectiveness and advantage by comparison with other control algorithms.