Papers
26
Total Citations
1,308
H-Index
13
About
Linghuan Kong is a prominent researcher specializing in intelligent control systems for robotic manipulators, with particular expertise in adaptive neural network and fuzzy logic control, fault-tolerant systems, and constrained robot motion. His work addresses some of the most challenging problems in modern robotics, including unknown system dynamics, actuator failures, input nonlinearities, and workspace constraints. Kong's most influential contribution, "Adaptive Fuzzy Control for Coordinated Multiple Robots With Constraint Using Impedance Learning" (2019, 285 citations), pioneered the use of fuzzy neural networks combined with impedance learning to enable multi-robot coordination under dynamic uncertainty — a breakthrough with significant implications for collaborative robotics. His broader body of work systematically advances neural network-based control strategies, incorporating techniques such as backstepping, barrier Lyapunov functions, sliding mode control, and adaptive dynamic programming to achieve robust, finite-time, and fixed-time convergence guarantees. More recently, Kong has extended his research into robot skill acquisition through dynamic movement primitives, reflecting a forward-looking interest in robot learning from demonstrations. With over 900 cumulative citations across his top publications and sustained output from 2019 to 2023, Kong has established himself as an impactful voice in intelligent robotics control, whose contributions are widely referenced by researchers advancing autonomous and adaptive robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5Robust Neurooptimal Control for a Robot via Adaptive Dynamic Programming91 citations · 2020
- 6
- 7Dynamic Movement Primitives Based Robot Skills Learning84 citations · 2023
- 8
- 9
- 10