Papers
11
Total Citations
215
H-Index
7
About
Narendra Kumar Dhar is a control systems and robotics researcher whose work sits at the intersection of advanced control theory, multi-agent systems, and human-robot interaction. His research has made substantial contributions to sliding mode control, event-triggered frameworks, and adaptive neural network-based control strategies for complex nonlinear systems. Dhar's most influential work, an event-triggered sliding mode control approach for nonlinear trajectory tracking (2019, 80 citations), established robust methods for managing disturbances in robotic manipulators while reducing computational overhead through event-based sampling. Complementing this, his adaptive neural network controller for MIMO nonaffine nonlinear systems with asymmetric time-varying state constraints (2019, 41 citations) demonstrated sophisticated barrier Lyapunov function-based backstepping designs applicable to interconnected systems. His research portfolio extends into multi-robot consensus and formation control under network uncertainties, incorporating stochastic and near-optimal sliding mode strategies. More recently, Dhar has expanded into human-robot interaction, contributing work on dynamic hand gesture recognition and bio-inspired electronic skin capable of multi-modal tactile sensing, signaling a compelling evolution toward intelligent, embodied robotics. Collectively accumulating over 200 citations, his body of work reflects a productive career bridging rigorous control theory with practical robotic applications.
Research Focus
Key Achievements
Top Papers
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- 7Event-Triggered Control for Trajectory Tracking by Robotic Manipulator7 citations · 2018
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- 9Dynamic Hand Gesture Recognition for Robot Manipulator Tasks4 citations · 2024
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