Papers
9
Total Citations
370
H-Index
6
About
Yan‐Wu Wang is a leading researcher in the field of networked robotic systems, with a primary focus on advanced control theory, formation tracking, and human-robot interaction. His most influential work, "Solving time-varying quadratic programs based on finite-time Zhang neural networks and their application to robot tracking" (128 citations), established a foundation for real-time robotic control using neural dynamics. Wang has made seminal contributions to bipartite and multiformation tracking problems, introducing hierarchical hybrid control algorithms that enable robots to achieve coordinated behavior despite input disturbances, parametric uncertainties, and signed communication graphs. His papers on lag-bipartite formation tracking (52 citations) and output multiformation tracking (56 citations) are widely cited for their innovative approaches to heterogeneous robotic systems. Wang has also advanced sliding mode control for nonlinear systems, including applications to pneumatic muscle actuators (42 citations), and explored self-triggered model predictive control for teleoperation. His recent work on dynamic-memory event-triggered dissipative control for fuzzy semi-Markov jump systems demonstrates his expanding influence into complex, networked control systems. With over 370 total citations and multiple high-impact publications from 2014 to 2024, Wang continues to shape the future of autonomous and collaborative robotics.
Research Focus
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Top Papers
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- 8Virtual simulation of communication between KUKA robot and PLC5 citations · 2020
- 9Predefined-time Terminal Sliding Mode Control of Robot Manipulators5 citations · 2020