Minh Le Thanh

Vinh Long University of Technology Education

Papers

Total Citations

H-Index

About

Minh Le Thanh is a researcher specializing in robotics, control systems, and intelligent automation, with a particular focus on the integration of neural networks and fuzzy logic for advanced robotic applications. His most notable contribution is the development of a recurrent fuzzy neural network (RFNN) approach to enhance PID controllers for delta robots, a critical advancement in high-speed, precision automation. This work, published in 2021, demonstrates how adaptive neural-fuzzy systems can significantly improve trajectory tracking and disturbance rejection in industrial robots, offering a robust solution for tasks like pick-and-place operations. While his research is still emerging, with his top-cited paper garnering 4 citations, it reflects a growing interest in hybrid control methodologies that bridge classical and intelligent systems. Minh’s work is particularly relevant for students and researchers exploring the intersection of soft computing and mechatronics, as it provides a practical framework for optimizing real-time control in complex, nonlinear environments. His contributions underscore the potential of combining recurrent neural networks with fuzzy logic to achieve superior performance in robotic systems, positioning him as a promising voice in the field of intelligent control.