Ahmad Kalhor
Papers
65
Total Citations
809
H-Index
15
About
Ahmad Kalhor is a prominent robotics and control systems researcher whose work spans parallel robots, cable-driven mechanisms, haptic devices, and intelligent control methodologies. His research has made significant contributions to the modeling, identification, and control of complex robotic systems, with a particular focus on parallel robot architectures including Gough–Stewart platforms, Delta robots, and cable-driven parallel robots. Kalhor's most cited work (75 citations) introduced an innovative Backstepping-Sliding Mode controller combined with a geometry-based quasi forward kinematic method for pneumatically actuated parallel robots, addressing one of the field's most challenging control problems. His extensive experimental investigations into cable-driven parallel robots — earning over 130 combined citations — have advanced both vision-based control strategies and oscillation damping techniques. His dynamic identification work on the Novint Falcon haptic device, accumulating over 60 citations across two studies, established rigorous white-box and model-based approaches for haptic systems. More recently, Kalhor has expanded into deep learning-based path planning (38 citations) and adaptive control for Delta robots, reflecting his evolving engagement with data-driven robotics. With research spanning humanoid balance strategies to sophisticated parallel manipulators, his cumulative impact — exceeding 390 citations — marks him as a versatile and influential voice in experimental and theoretical robotics.
Research Focus
Key Achievements
Top Papers
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- 9Dynamic identification of the Novint Falcon Haptic device23 citations · 2016
- 10Push recovery for NAO humanoid robot19 citations · 2014