Chung-Wei Wu
Papers
2
Total Citations
27
H-Index
2
About
Chung-Wei Wu is a leading researcher in advanced multi-robot systems, specializing in cooperative control, formation navigation, and intelligent localization for heterogeneous robotic networks. His work bridges robust nonlinear control theory with emerging machine learning architectures, notably the broad learning system (BLS). In his most-cited paper (2019, 15 citations), Wu introduced an adaptive distributed fractional-order nonsingular terminal sliding-mode (FONTSM) controller integrated with BLS, enabling uncertain heterogeneous omnidirectional mobile multi-robots (HOMRs) to achieve precise formation control despite dynamic disturbances. This approach significantly improves convergence speed and robustness over conventional sliding-mode methods. His second major contribution (2019, 12 citations) presents a cooperative localization framework combining fuzzy decentralized differential evolution information filter (DDEIF) with BLS, enhancing state estimation accuracy for multi-robot teams operating under uncertainty. Wu’s work is notable for its practical emphasis on real-world implementation challenges—such as dynamic effects, sensor noise, and communication constraints—making his methods highly relevant for autonomous warehouse, search-and-rescue, and industrial inspection applications. With a growing citation record, Wu is establishing himself as a key innovator at the intersection of fractional-order control, learning systems, and cooperative robotics.
Research Focus
Key Achievements
Top Papers
- 1
- 2