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About

Vahid Meigoli is a robotics researcher whose work focuses on the control and design of robotic manipulators for rehabilitation and industrial applications. His primary research areas include sliding mode control, adaptive fuzzy-neural systems, and exoskeleton robotics. Meigoli’s most cited paper, “Sliding mode control of an exoskeleton robot for use in upper-limb rehabilitation” (2015, 39 citations), introduces a novel 3-degree-of-freedom exoskeleton for shoulder rehabilitation after stroke, combining mechanical design with robust control strategies. This work has been influential in advancing assistive robotics for post-stroke therapy. His second most cited paper, “Dynamic analysis, simulation, and control of a 6-DOF IRB-120 robot manipulator using sliding mode control and boundary layer method” (2018, 32 citations), demonstrates his expertise in industrial robotics, offering practical solutions for reducing chattering in sliding mode control. More recently, his 2023 paper on adaptive fuzzy-neural inference systems (20 citations) highlights his push toward intelligent, adaptive control approaches. With over 90 total citations across his key works, Meigoli’s contributions bridge theoretical control methods and real-world robotic applications, making his research valuable for students and engineers working in rehabilitation robotics and nonlinear control systems.