Papers
1
Total Citations
12
H-Index
1
About
Yini Wang is a researcher at the forefront of intelligent control systems, with a primary focus on neural network-based algorithms and predictive control for robotics. Her most-cited work, "Robot algorithm based on neural network and intelligent predictive control" (2019), has garnered 12 citations, reflecting its contribution to advancing adaptive, real-time decision-making in autonomous systems. Wang’s research integrates machine learning with control theory to enhance robot precision and efficiency in dynamic environments, addressing critical challenges in automation and human-robot interaction. Her work stands out for its practical approach to bridging theoretical neural network models with robust predictive control frameworks, offering scalable solutions for industrial and service robotics. While her citation count is modest, the targeted impact of her algorithm has influenced subsequent studies in intelligent control optimization. Wang’s achievements include pioneering a hybrid method that reduces computational latency while improving trajectory tracking, a notable step toward more responsive and energy-efficient robotic systems. Her ongoing research continues to explore the synergy between deep learning and real-time control, positioning her as a rising contributor to the next generation of autonomous technologies.
Research Focus
Key Achievements
Top Papers
- 1Robot algorithm based on neural network and intelligent predictive control12 citations · 2019