Farid Sheikholeslam
Papers
17
Total Citations
300
H-Index
9
About
Farid Sheikholeslam is a prominent researcher in robotics and intelligent control systems, with particular expertise in adaptive control, impedance control, and neural network-based approaches for robotic manipulators. His work has consistently pushed the boundaries of how robots interact with uncertain and dynamically changing environments. Sheikholeslam's most significant contributions lie in developing sophisticated force and position control frameworks for robotic systems. His pioneering work on wavelet neural networks applied to impedance control — most notably his 2021 paper garnering 71 citations — demonstrates how intelligent learning architectures can enable robots to track contact forces reliably in unknown environments. Complementing this, his recurrent fuzzy wavelet neural network approach (2020, 45 citations) introduced innovative variable impedance control strategies that adapt dynamically to environmental uncertainties. A recurring theme across his research is sensorless control — developing adaptive force estimators that eliminate the need for costly physical sensors while maintaining precision and stability. His 2015 study on adaptive hybrid force/position control (32 citations) exemplifies this philosophy elegantly. Early contributions addressing fault detection in nonlinear systems (2014, 40 citations) and robust control under unmodeled dynamics further demonstrate his breadth. With cumulative citations exceeding 270 across his major works, Sheikholeslam has established himself as a meaningful contributor to intelligent robotic control, offering practical solutions to real-world challenges in human-robot interaction and autonomous manipulation.
Research Focus
Key Achievements
Top Papers
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- 7An Adaptive Manipulator Controller Based on Force and Parameter Estimation16 citations · 2006
- 8A new path planner for autonomous mobile robots based on genetic algorithm13 citations · 2010
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