Farzad Towhidkhah
Amirkabir University of Technology, University of Saskatchewan
Papers
12
Total Citations
208
H-Index
7
About
Farzad Towhidkhah is a leading researcher in the fields of human motor control, robotics, and intelligent control systems, with a particular focus on impedance control and teleoperation. His most influential work, "Learning to Control Arm Stiffness Under Static Conditions" (91 citations), fundamentally advanced our understanding of how humans adapt and control limb impedance through a learning process, using robotic devices to reveal the voluntary control of hand stiffness patterns. This foundational research has shaped subsequent studies in human-robot interaction and rehabilitation robotics. Towhidkhah has made significant contributions to the control of flexible-link manipulators, developing innovative approaches that combine Extended Kalman Filters for environmental force observation with Lyapunov redesign robust control to achieve precise tracking despite disturbances. His work on dual-user teleoperation systems addresses critical challenges in cooperative robotic operations, particularly the destabilizing effects of time-delay. Through his research on Model Predictive Impedance Control (MPIC), he has provided novel insights into human walking control on rough terrains, bridging the gap between robotic control theory and biological motor control. His diverse portfolio of over 200 citations demonstrates sustained impact across multiple domains of robotics and human movement science.
Research Focus
Key Achievements
Top Papers
- 1Learning to Control Arm Stiffness Under Static Conditions91 citations · 2004
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- 8Transfer and durability of acquired patterns of human arm stiffness7 citations · 2005
- 9Control challenges in non-minimum phase tele-robotics systems6 citations · 2011
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