Papers
21
Total Citations
319
H-Index
10
About
Abdul Manan Khan is a distinguished robotics and control systems researcher whose work spans rehabilitation robotics, exoskeleton design, human motion analysis, and bio-inspired robotic systems. He has made landmark contributions to the field of upper limb assist exoskeletons, developing sophisticated control strategies including passivity-based adaptive control, adaptive impedance control, and robust sliding mode control to enable seamless human-robot interaction during rehabilitation. His 2015 and 2016 studies on adaptive impedance and compliance control have collectively garnered over 100 citations, reflecting their foundational influence on assistive robotics research. Khan's work on estimating Desired Motion Intention — using techniques such as Extreme Learning Machines — addresses one of rehabilitation robotics' most challenging problems: predicting human intent in real time to deliver responsive, patient-centered assistance. Beyond upper limb systems, his research extends to lower limb biomechanics, where his 2023 LSTM-based model for human gait analysis and inverse kinematics has rapidly attracted 42 citations. He has also explored modular snake robots, dexterous Shape Memory Alloy-actuated robotic hands, and wearable strength-assist systems. With over 265 cumulative citations across diverse robotics domains, Khan represents a versatile and impactful voice in intelligent rehabilitation and bio-inspired robotic engineering.
Research Focus
Key Achievements
Top Papers
- 1Passivity based adaptive control for upper extremity assist exoskeleton48 citations · 2016
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- 3Adaptive impedance control for upper limb assist exoskeleton41 citations · 2015
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- 6A Spiral Curve Gait Design for a Modular Snake Robot Moving on a Pipe24 citations · 2019
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