In this article, an adaptive robust iterative learning control is developed to solve the trajectory tracking problem of a parallel Delta robot performing repetitive tasks and subjected to external disturbances. The proposed control scheme is composed of an adaptive proportional–derivative controller to increase the convergence rate, a proportional–derivative-type iterative learning control to enhance the tracking performances through the repetitive trajectory as well as a robust term to compensate the repetitive and nonrepetitive disturbances. The practical assumption of alignment condition is introduced instead of the classical assumption of resetting conditions. The asymptotic convergence is proved using Lyaponuv analysis, and it is shown that the tracking error decreases through the iterations. Simulation and experiments are performed on a Delta robot to demonstrate the effectiveness and the superiority of the proposed controller over the traditional iterative learning control.

中文翻译：

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应用于Delta机器人的自适应鲁棒迭代学习控制

在本文中，开发了一种自适应鲁棒迭代学习控制来解决并行 Delta 机器人执行重复任务并受到外部干扰的轨迹跟踪问题。所提出的控制方案由用于提高收敛速度的自适应比例微分控制器、通过重复轨迹增强跟踪性能的比例微分型迭代学习控制以及用于补偿重复和非重复干扰的鲁棒项组成. 引入了对齐条件的实际假设，而不是重置条件的经典假设。使用Lyaponuv分析证明了渐近收敛性，表明跟踪误差通过迭代减小。