David Ofosu Amoateng
University of Electronic Science and Technology of China, Technology Innovation Institute
Papers
2
Total Citations
465
H-Index
2
About
David Ofosu Amoateng is a leading researcher in intelligent robotic control systems, with a primary focus on adaptive neural network control for complex, nonlinear robotic manipulators. His work addresses critical challenges in automation, particularly the compensation of actuator imperfections such as input deadzone, output constraints, and backlash-like hysteresis. Amoateng’s most influential contribution, the 2015 paper “Neural Network Control of a Robotic Manipulator With Input Deadzone and Output Constraint,” has garnered 396 citations, establishing a foundational framework for using barrier Lyapunov functions alongside adaptive neural networks to ensure safe, constrained motion. He further advanced the field with a 2016 study (69 citations) that tackled the difficult problem of backlash-like hysteresis in 3-DOF manipulators, employing dual radial basis function neural networks to approximate both system dynamics and nonlinear friction. This work is vital for improving precision in industrial robotics and prosthetics. Amoateng’s research is distinguished by its rigorous integration of Lyapunov stability theory with machine learning, offering practical solutions for high-performance robotic systems operating under real-world physical constraints.
Research Focus
Key Achievements
Top Papers
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