Minh-Due Tran
Papers
1
Total Citations
7
H-Index
1
About
Minh-Due Tran is a robotics and control systems researcher whose work centers on intelligent control strategies for robotic manipulators operating under real-world constraints. His most-cited paper, "A Neural-network-based Nonlinear Controller for Robot Manipulators with Gain-learning Ability and Output Constraints" (2021, 7 citations), introduces an adaptive robust controller that combines neural-network learning with a modified backstepping scheme to handle uncertain nonlinearities while respecting output constraints. This contribution addresses a critical challenge in modern robotics: enabling precise, safe motion in environments where physical limits cannot be violated. Tran’s approach stands out for its gain-learning capability, allowing the controller to adapt online without requiring exact system models. Though his citation count is modest, his work is technically significant for bridging neural-network approximation and constraint-aware control design—a growing priority in human-robot interaction and industrial automation. His research appeals to engineers seeking practical, implementable solutions for nonlinear systems with safety-critical boundaries.
Research Focus
Key Achievements
Top Papers
- 1