Rahmad Junaidi
Papers
1
Total Citations
2
H-Index
1
About
Rahmad Junaidi is a researcher focused on robotics, control systems, and intelligent computational modeling. His work centers on improving robotic motion prediction and control through the integration of mathematical modeling and adaptive learning systems. His most cited paper, “Determining the arm's motion angle using inverse kinematics models and adaptive neuro-fuzzy interface system” (2021), introduces a novel approach that combines inverse kinematics with an adaptive neuro-fuzzy inference system (ANFIS) to enhance the accuracy of robotic arm movement. This contribution addresses a critical challenge in robotics: predicting motion with precision using learning-based logic. While his citation count is still growing, Junaidi’s work represents an important step toward more intelligent and responsive robotic systems. His research is particularly relevant for students and engineers exploring the intersection of kinematics, fuzzy logic, and neural networks in automation. By bridging theoretical models with adaptive algorithms, Junaidi contributes to the development of smarter, more autonomous robots capable of complex tasks in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1