Mohammad Pourrahim
Papers
2
Total Citations
11
H-Index
2
About
Mohammad Pourrahim’s research focuses on advanced nonlinear control strategies for robotic manipulators, with a particular emphasis on trajectory tracking, saturation control, and intelligent observer-based systems. His major contributions lie in the experimental validation of theoretically robust controllers on industrial hardware, bridging the gap between simulation and real-world application. His most cited work, “Experimental evaluation of a saturated output feedback controller using RBF neural networks for SCARA robot IBM 7547” (2016, 8 citations), extends passivity-based output feedback control by integrating radial basis function neural networks to handle actuator saturation, demonstrating improved precision on a physical SCARA platform. In a related study, “Fuzzy gain scheduling saturated PID controller: Design and implementation on robot manipulator” (2017, 3 citations), he developed a fuzzy-tuned saturated PID controller that adapts gains in real time for enhanced trajectory accuracy. Beyond these papers, Pourrahim’s hands-on work includes designing, constructing, and implementing the electrical and control systems for the IBM 7547 robot, showcasing a rare combination of theoretical depth and practical engineering skill. His research has direct implications for industrial automation, where reliable, saturated control is critical for safety and performance.
Research Focus
Key Achievements
Top Papers
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