Papers
9
Total Citations
1,018
H-Index
9
About
Yuncheng Ouyang is a control systems researcher specializing in intelligent control of robotic manipulators, with particular expertise in neural network-based adaptive control, reinforcement learning, and vibration suppression. His work addresses some of the most persistent challenges in robotics, including input nonlinearities such as deadzone and backlash-like hysteresis, system uncertainties, and state constraints in both single-link and dual-arm manipulator systems. Ouyang's most influential contribution — "Vibration Control of a Flexible Robotic Manipulator in the Presence of Input Deadzone" (2016, 385 citations) — demonstrated how neural networks could simultaneously approximate unknown system dynamics and compensate for actuator imperfections, establishing a foundational framework widely adopted in the field. His follow-up work on adaptive neural control with barrier Lyapunov functions (2018, 321 citations) advanced constraint-satisfying control methods for uncertain robotic systems. He has also pioneered the application of reinforcement learning and actor-critic architectures to flexible manipulator control, bridging optimal control theory with practical robotic applications. Across his portfolio, Ouyang has accumulated over 1,000 citations, reflecting strong and sustained influence on the robotics and intelligent control communities. His research consistently translates rigorous theoretical frameworks into practical solutions for real-world robotic challenges.
Research Focus
Key Achievements
Top Papers
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- 3Reinforcement learning control of a single‐link flexible robotic manipulator91 citations · 2017
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