An innovative AR-assisted self-adjusting digital twin for speed control of a servomechanism

Abstract

A machine might undergo changes in its system due to prolonged operation. These changes can significantly impact the performance and behavior of the machine’s controller. The general digital twin solutions typically use compensators to adjust the physical entity instead of updating the internal model parameters of the digital twin. Therefore, if deviations are significant, the physical entity may never fully match its digital twin. To address potential degradation in controller performance, this study introduces an AR (augmented reality)-assisted self-adjusting digital twin (SADT) servomechanism monitoring system. The primary objective of this system is to provide real-time monitoring, maintenance, and adjustments to ensure the optimal operation of the machine’s controller, even in the presence of significant deviations caused by extended operation or environmental changes. In smart manufacturing, servomechanisms are often used in wheeled mobile robots and conveyor mechanisms. Hence, this study selects servomechanisms as the target application. The AR-assisted SADT accomplishes this by comparing the angular velocity error between the servomechanism and its digital twin to identify when significant changes have occurred. Upon detecting such a change, the SADT calculates new internal model parameters, generating a modified digital twin. This allows resynchronization with the changed physical entity, restoration of the virtual-physical mapping, and continuous, accurate monitoring and optimized control. Furthermore, to enhance human–machine interaction, AR technology is used to offer users visual information, enabling intuitive monitoring of the servomechanism’s performance. In summary, the AR-assisted SADT system makes human–machine interaction more intuitive for operators and enhances the adaptability and accuracy of system monitoring.