Edy Riyanto
Papers
1
Total Citations
4
H-Index
1
About
Dr. Edy Riyanto is a control systems engineer whose research focuses on advanced motor synchronization and neural network-based friction compensation. His most-cited work, “Synchronization of Dual Servo Motor Using CMAC Neural Network-based Lugre Friction Model” (2021), addresses a critical challenge in precision motion control for applications ranging from electric vehicles and robotics to industrial electronics manufacturing. By integrating a cerebellar model articulation controller (CMAC) neural network with the Lugre friction model, Riyanto developed a novel approach to mitigate nonlinear friction effects and improve synchronization accuracy in dual servo systems—a problem that directly impacts performance in high-precision automation. Though his citation count is currently modest, his contribution lies in bridging neural network adaptability with classical friction modeling, offering a practical solution for real-time control in multi-motor setups. This work underscores his commitment to advancing intelligent control strategies for mechatronic systems, positioning him as a promising researcher in the intersection of neural networks and servo control. His research holds particular relevance for engineers developing next-generation autonomous and manufacturing technologies.
Research Focus
Key Achievements
Top Papers
- 1