Guanghui Sun
Papers
13
Total Citations
454
H-Index
10
About
Guanghui Sun is a prominent robotics and control systems researcher whose work spans intelligent control, cyber-physical security, and advanced robot systems. His research uniquely bridges classical control theory with modern machine learning, particularly deep reinforcement learning, to tackle complex real-world challenges in robotics. Sun has made significant contributions to robot security, developing frameworks for detecting and countering cyberattacks on robotic systems, with his work on actuator attack mitigation and secure robot learning accumulating over 140 citations combined. His investigations into cable-driven parallel robots and continuum soft robots address some of the most demanding challenges in flexible robotic control, combining model-based strategies with learning approaches to handle system uncertainties and input constraints. His earlier foundational work on dual terminal sliding mode control for rigid manipulators (84 citations) established his credentials in robust control design. Beyond individual robot platforms, Sun has contributed to multi-robot coordination, lifelong mapping in dynamic environments, and flexible spacecraft dynamics modeling, reflecting a remarkably broad research vision. His 2024–2025 publications on fractional-order control and transformer-based optimization suggest he continues pushing methodological frontiers. With over 440 cumulative citations, Sun's work represents a vital intersection of control engineering, artificial intelligence, and robotics security.
Research Focus
Key Achievements
Top Papers
- 1Deep reinforcement learning control approach to mitigating actuator attacks89 citations · 2023
- 2Dual terminal sliding mode control design for rigid robotic manipulator84 citations · 2017
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