Fangfang Zhang
Zhengzhou University, Henan University of Science and Technology
Papers
12
Total Citations
139
H-Index
7
About
Fangfang Zhang is a leading researcher in the field of multi-robot systems and intelligent control, with a primary focus on bio-inspired neural networks, sliding mode control, and safety-critical robotics. Her most significant contributions lie in developing novel algorithms for multi-robot cooperative area coverage and search tasks in unknown and complex obstacle environments, as exemplified by her highly cited works on Glasius bio-inspired neural networks (GBNN) and dual-improved BNNs (DIBNN), which have collectively garnered over 100 citations. Zhang has also advanced the control of soft actuators through observer-based adaptive sliding mode control (37 citations) and pioneered hybrid position/force tracking for robotic systems without velocity measurement (21 citations). Her recent work on high-order control barrier functions for constrained robotic systems (10 citations) addresses critical challenges in maintaining system smoothness and avoiding control input chattering. Additionally, she has made notable contributions to multi-robot pattern formation and dynamic obstacle avoidance using iterative optimization and artificial potential field methods. Her research is characterized by a practical, algorithm-driven approach that directly addresses real-world constraints in autonomous robotics, making her work essential reading for researchers in distributed robotics and intelligent control systems.
Research Focus
Key Achievements
Top Papers
- 1Observer-based continuous adaptive sliding mode control for soft actuators37 citations · 2021
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