Synchronized Coupled Iterative Learning Control Addressing the Nonlinear Disturbances in Gantry Platforms

The demand for high-precision and high-efficiency motion control in modern industrial applications such as laser processing, photolithography, and nanotechnology has highlighted critical challenges in multi-axis control systems, particularly regarding synchronization and coupling dynamics. Traditional single-axis control approaches often neglect the mechanical coupling effects that arise from system structure, thereby limiting overall performance. To address this limitation, this paper proposes a novel Synchronized Coupled Iterative Learning Controller (SCILC) for systems driven by iron-core linear motors that inherently suffer from nonlinear disturbances such as friction and cogging force. By incorporating a coupled system model and embedding synchronization error into the learning framework, SCILC effectively accounts for inter-axis dynamics and enhances control performance. Comparative experimental results demonstrate that the proposed SCILC significantly outperforms conventional multi-loop control and Coupled Control Iterative Learning Control (CCILC) methods. Specifically, the root mean square (RMS) tracking error is reduced from 128.917 μm (traditional control) to 3.255 μm (CCILC), and further down to 1.641 μm using SCILC. In terms of synchronization, the RMS error decreases from 1.39 μrad to 0.98 μrad, achieving a 28.7% improvement. These findings validate that SCILC offers superior convergence, precision tracking, and synchronization accuracy, thereby making it well-suited for high-end industrial applications.