Mohamed Zohdy
Papers
10
Total Citations
68
H-Index
4
About
Mohamed Zohdy is a researcher whose career spans several decades of contributions at the intersection of control systems, robotics, and intelligent learning algorithms. His foundational work in the late 1980s established him as a pioneering voice in robust robotic manipulator control, addressing a critical gap in the field: the real-world unreliability of assuming perfectly known system parameters. His papers on computed torque schemes and robust pole assignment — exploring both deterministic and stochastic stability robustness — laid important groundwork for more resilient robot controller design, collectively drawing over 20 citations. Zohdy's research evolved meaningfully into reinforcement learning-based control, with his most-cited work, "Reinforcement Learning Control of Nonlinear Multi-link Systems" (2001, 30 citations), demonstrating how adaptive learning strategies can govern complex nonlinear robotic systems without explicit knowledge of system dynamics. His continued exploration of dexterous robot control and truck-trailer autonomous parking — through recurrent proximal policy optimization and trajectory-state reinforcement learning — reflects a sustained commitment to bridging theoretical control methods with practical autonomous systems challenges. More recently, he has engaged with cybersecurity vulnerabilities in cyber-physical systems, broadening his impact into critical infrastructure protection. Across his career, Zohdy exemplifies a researcher who consistently pursues robust, intelligent solutions to dynamic real-world control problems.
Research Focus
Key Achievements
Top Papers
- 1Reinforcement learning control of nonlinear multi-link system30 citations · 2001
- 2Robust Pole Assignment for Computed Torque Robotic Manipulators Control13 citations · 1989
- 3Deterministic and Stochastic Robustness of the Computed Torque Scheme5 citations · 1990
- 4
- 5
- 6Robust Control of Robotic Manipulators3 citations · 1989
- 7Application of reinforcement learning to dexterous robot control3 citations · 1998
- 8
- 9
- 10