Edward G. Tan
Papers
2
Total Citations
21
H-Index
2
About
Edward G. Tan has made pioneering contributions to the field of intelligent robotics, focusing on adaptive neural network control for flexible robotic systems. His research addresses the fundamental challenge of controlling robots with structural flexibility—a critical issue for precision and safety in advanced manufacturing and service robotics. Tan’s most cited work, "Adaptive neural network control of flexible joint robots based on feedback linearization" (1998, 17 citations), introduced a robust controller that uses an additional neural network to enhance a fixed nonlinear baseline, enabling real-time adaptation to dynamic uncertainties. He further advanced the field with "Adaptive neural network control of flexible link robots based on singular perturbation" (2002, 4 citations), where he decomposed the complex full model of flexible link robots into separate time-scale subsystems, treating elastic forces as fast variables and joint variables as slow ones. This elegant approach allowed for more tractable and effective control design. Though his citation counts are modest, Tan’s work is notable for its theoretical rigor and practical relevance, laying groundwork for later developments in adaptive and learning-based control of underactuated and flexible robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2