Papers
16
Total Citations
172
H-Index
8
About
Fujie Wang is a robotics and control systems researcher whose work sits at the intersection of adaptive control theory, visual servoing, and intelligent learning methods for robotic systems. With a career spanning nearly a decade of active publication, Wang has made significant contributions to solving some of the most persistent challenges in robot control, including unknown actuator nonlinearities such as dead zones, hysteresis, quantized saturation inputs, and uncertain system dynamics. Wang's foundational research focused on adaptive fuzzy and neural network-based visual servoing controllers for robotic manipulators, with early papers from 2016 to 2019 accumulating over 100 combined citations. These works addressed real-world imperfections in camera calibration, robot kinematics, and actuator behavior, delivering robust solutions applicable to industrial manipulation tasks. More recently, Wang has embraced deep reinforcement learning, developing novel approaches that combine techniques such as Soft Actor-Critic, Generative Adversarial Imitation Learning, and LSTM networks to handle trajectory tracking without requiring explicit system models. Wang has also contributed to teleoperation and multi-robot coordination under communication delays and event-triggered control frameworks. Collectively, this body of work reflects a researcher continuously evolving at the frontier of intelligent, adaptive robotics control.
Research Focus
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