The fourth industrial revolution led to advanced servo systems, enhancing productivity across industries. However, designing these systems remains challenging due to the performance-stability trade-off. This paper presents a model-based motion control of a linear motor motion stage in frequency domain. A user-code for the PowerPMAC commercial controller was developed to excite motion control system so that we could get a frequency response. The theoretical frequency response of the servo algorithm was compared with the experimental frequency response. Based on this, a tuning graphical user interface (GUI) was developed to predict performance when the servo loop gain is changed. Especially, to compensate for residual vibrations caused by high acceleration and deceleration and to improve tracking error, DOB (Disturbance Observer) and ILC (Iterative Learning Control) control techniques were applied in the frequency domain. Through the design of the frequency domain motion controller, the control performance of the linear motor motion stage could be predicted with over 96% accuracy, resulting in a 54.32% improvement in tracking error and a 93.56% improvement in settling time, 85.29% in RMS error.

Transfer robots for large-sized panels used in the display industry need to compensate for path error and reduce vibration. The iterative learning control (ILC) technique can simply compensate for the uncertainty of a control system in a repetitive motion. This study introduces an ILC compensation system applied with an accelerometer to a display panel transfer robot control system. The ILC technique was used to reduce the path error and vibration induced the flexibility of the large size robot. This method was applied to a robot system without the system model of the mechanical and measurement elements. To improve the iterative learning performance through the accelerometer, the ILC is configured by applying an acceleration element and time shift method to the PD-Offline ILC algorithm. In addition, based on the characteristics of repetitive motion, the ILC derives an acceleration data-based position estimation value. In this study, the ILC system and a large-sized panel transfer robot were implemented in MATLAB-Simulink with RECURDYN. The path errors and vibration level of the robot with a suggested ILC of 20 repeated learnings were reduced by more than 90%.