Seyed Mohammad Ahmadi
Papers
14
Total Citations
237
H-Index
10
About
Seyed Mohammad Ahmadi is a robotics and control systems researcher whose work centers on robust and adaptive control of robotic systems, with particular emphasis on uncertainty estimation, task-space control, and the dynamics of electrically driven manipulators and mobile robots. His research addresses one of the most persistent challenges in robotics: designing reliable controllers that perform well despite parametric and non-parametric uncertainties inherent in complex mechanical systems. Ahmadi's most influential contributions include the development of adaptive Taylor series-based uncertainty estimators, which leverage the universal approximation properties of Taylor series to achieve asymptotic tracking control in both joint and task spaces. His work on Radial Basis Function (RBF) network control introduced a decentralized, model-free approach that garnered 31 citations, while his Taylor series and robust control frameworks each attracted 33 citations. He has further extended these methodologies to wheeled mobile robots, impedance control, and finite-time adaptive backstepping strategies, demonstrating remarkable breadth across robotic platforms. Collectively accumulating over 200 citations, Ahmadi's body of work has meaningfully advanced the field of intelligent robot control, offering practical frameworks that bridge theoretical rigor with real-world applicability — making his research particularly valuable to engineers and academics working on next-generation autonomous robotic systems.
Research Focus
Key Achievements
Top Papers
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- 3Adaptive RBF network control for robot manipulators31 citations · 2014
- 4A state augmented adaptive backstepping control of wheeled mobile robots26 citations · 2020
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- 6On the Taylor series asymptotic tracking control of robots17 citations · 2018
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