Papers
4
Total Citations
66
H-Index
4
About
Runxian Yang is a robotics and control systems researcher whose work centers on intelligent adaptive control for robot manipulators operating under uncertain and dynamically complex conditions. Yang's primary contributions lie in the development of radial basis function neural network (RBFNN)-based adaptive control strategies and impedance control frameworks designed to handle the inherent nonlinearities and unknown dynamics that challenge real-world robotic systems. Among Yang's most recognized contributions is a 2016 study on discrete-time optimal adaptive RBFNN control for robot manipulators, which has garnered 37 citations and established a foundational framework for applying neural network compensation in uncertain robotic environments. Complementing this, Yang's 2017 work on adaptive impedance control integrating Q-learning and disturbance observers — cited 18 times — demonstrates a forward-thinking fusion of reinforcement learning and classical control theory to enable robust force interaction in time-varying, unknown environments. Additional work has explored finite-time convergence in RBFNN-based controllers and trajectory tracking for multi-DOF manipulators, reflecting a consistent focus on practical, computationally efficient solutions. Collectively, Yang's research contributes meaningfully to the advancement of intelligent, adaptive robotic control in both theoretical and applied contexts.
Research Focus
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