Iterative Learning Control for Deformable Open-Frame Cable-Driven Parallel Robots

This paper proposed an iterative learning control (ILC) scheme for deformable open-frame cable-driven parallel robots (D-CDPRs). In contrast to the straightforward inverse kinematics of the rigid frame cable-driven parallel robots (CDPRs), accurate modeling of the deformable frame poses challenges due to errors and uncertainties. To address these issues, the authors propose the use of ILC, a control strategy that modifies the control input over iterations based on previous results. ILC has been successfully applied to traditional cable robots, particularly in handling model uncertainty. The paper presents a novel ILC control scheme specifically designed for D-CDPRs, with a focus on reducing tracking errors over repetitive operations. Additionally, hardware experiments are conducted to validate the effectiveness and reliability of the proposed ILC approach. The results demonstrate the efficacy of ILC in mitigating tracking errors, even in scenarios where the dynamic model of the D-CDPRs is unknown.