Rofiqul Umam
Papers
1
Total Citations
2
H-Index
1
About
Rofiqul Umam is a researcher whose work bridges robotics, control systems, and computational intelligence. His primary research areas include robotic motion control, inverse kinematics modeling, and adaptive neuro-fuzzy inference systems (ANFIS). Umam’s most cited work, “Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system” (2021), demonstrates his contribution to improving robot arm precision by integrating mathematical motion prediction with learning-based logic. This approach enhances the adaptability and accuracy of robotic systems, addressing a critical challenge in automation and human-robot interaction. While his citation count is still growing—with this paper garnering 2 citations to date—his work represents a foundational step toward smarter, more responsive robotic control. Umam’s research is particularly relevant for students and engineers interested in the intersection of robotics, artificial intelligence, and mechatronics, offering practical insights into how hybrid models can optimize real-world motion planning. As the demand for advanced robotics continues to rise, Umam’s contributions help pave the way for more intuitive and efficient robotic systems.
Research Focus
Key Achievements
Top Papers
- 1