Hongbin Wang
Papers
16
Total Citations
190
H-Index
7
About
Hongbin Wang is a robotics and control systems researcher whose work spans intelligent control algorithms, parallel robot mechanics, and autonomous mobile systems. His most significant contributions lie at the intersection of advanced control theory and practical robotic applications, with particular focus on redundantly actuated parallel robots and mobile robot navigation. Wang's research on model predictive control for 5-DOF parallel robots (46 citations) and deep learning-based obstacle avoidance for mobile robots (37 citations) demonstrates his ability to bridge theoretical frameworks with real-world robotic challenges. His hybrid force/position control strategies for parallel robots—explored across multiple publications—represent a sustained effort to improve precision and stability in complex mechanical systems. Equally notable is Wang's work in fuzzy systems theory, where he has tackled difficult problems involving T-S fuzzy models with time-varying delays, unmatched disturbances, and interval type-2 uncertainty (23 citations combined across several papers). His bioinspired neurodynamics approach to finite-time mobile robot control further highlights his interdisciplinary methodology, drawing from biological principles to address unmeasurable system states. With a body of work accumulating over 160 citations, Wang has established himself as a productive contributor to intelligent robotics, offering students and researchers valuable frameworks for robust, adaptive control in both parallel and mobile robotic platforms.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5
- 6
- 7
- 8
- 9
- 10Discrete PID-Type Iterative Learning Control for Mobile Robot7 citations · 2016