Tire-related crashes account for a large proportion of all types of car accidents. The causes of tire-related accidents are inappropriate tire temperature, pressure, and wear. Although temperature and pressure can be monitored easily with TPMS, there exists no system to monitor tire wear regularly. This paper proposes a system that can estimate tire wear using a 3-axis accelerometer attached to the tread inside the tire. This system utilizes axial acceleration, extracts feature from data acquired with the accelerometer and estimates tire wear by feature classification using machine learning. In particular, the proposed tire wear estimation method is designed to estimate tread depth in four types (7, 5.6, 4.2, and 1.4 mm) at speeds of 40, 50, and 60 kmph. Based on the data obtained during several runs on a test track, it has been found that this system can estimate the tread depth with reasonable accuracy.

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%.