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About

Seyed Ali Mohamad Dehghan is a robotics and control systems researcher whose work centers on adaptive control, force estimation, teleoperation, and rehabilitation robotics. He has made significant contributions to the field of sensorless force control, developing innovative methods that eliminate the need for expensive and fragile force sensors in robotic systems. His most influential work, "Adaptive hybrid force/position control of robot manipulators using an adaptive force estimator" (2015, 32 citations), introduced a position-based adaptive force estimator combined with an environment compliance estimator, offering a robust alternative to disturbance observer-based approaches. Building on this foundation, his 2020 study on observer-based adaptive force-position control for bilateral teleoperation with time delay (30 citations) extended these principles to complex remote manipulation scenarios. His earlier research on adaptive wavelet neural networks for force-environment estimation (2014, 23 citations) demonstrated his interest in intelligent, learning-based control strategies. Dehghan has also applied his expertise to healthcare, proposing a sensor-reduced method for estimating human arm mechanical impedance in rehabilitation robots (2018), highlighting the translational potential of his work. Across his portfolio, his research consistently advances safer, more cost-effective, and adaptable robotic control solutions.