Saeed Pezeshki
Papers
2
Total Citations
14
H-Index
2
About
Saeed Pezeshki’s research centers on advanced control systems for robotic manipulators, with a focus on enhancing performance under uncertainty and disturbance. His major contributions include the development of a novel Neuro-PID controller that integrates neural networks with classical PID control to address the challenges of open-loop instability and model uncertainties in robot manipulators. This work, detailed in his most-cited paper (12 citations), demonstrates a practical approach to improving system robustness. He has also systematically evaluated three nonlinear control methods—incorporating integral action and tracking controllers—to reject constant bounded disturbances, achieving improved tracking accuracy in robotic systems. While his citation counts reflect a specialized, emerging impact, his research provides foundational insights for students and engineers working on adaptive and robust control in robotics. Pezeshki’s work is notable for bridging theoretical control design with real-world robotic applications, offering clear benchmarks for disturbance rejection and performance optimization. His contributions are particularly valuable for those exploring intelligent control strategies in nonlinear, uncertain environments.
Research Focus
Key Achievements
Top Papers
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