Suprapto Suprapto

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About

Suprapto Suprapto is a researcher whose work lies at the intersection of advanced control systems, neural networks, and mechatronics, with a particular focus on precision motion control. His key research areas include servo motor synchronization, friction compensation, and the application of cerebellar model articulation controller (CMAC) neural networks to real-world engineering challenges. In his most cited work, "Synchronization of Dual Servo Motor Using CMAC Neural Network-based Lugre Friction Model" (2021, 4 citations), Suprapto addresses a critical issue in modern automation: the precise coordination of dual servo motors. This problem is central to applications in electric vehicles, robotics, and electronics manufacturing. His major contribution lies in integrating a CMAC neural network with the Lugre friction model to improve synchronization accuracy and robustness, overcoming limitations of traditional controllers. While his citation count is still growing, this work demonstrates his ability to bridge theoretical neural network methods with practical engineering needs. Suprapto’s research is particularly valuable for students and engineers working on high-precision motion systems, offering a novel approach to friction compensation that enhances performance in multi-motor setups.