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About

Minh-Duc Tran is a robotics and control systems researcher whose work centers on intelligent control strategies for robotic manipulators, with a particular emphasis on sliding mode control, adaptive algorithms, and neural network-based approaches. His most influential contribution, "Adaptive Terminal Sliding Mode Control of Uncertain Robotic Manipulators Based on Local Approximation of a Dynamic System" (2016), has garnered 98 citations, establishing him as a notable voice in robust control design for systems operating under uncertainty. Complementing this, his 2016 work introducing nonsingular terminal sliding mode control integrated with Radial Basis Function (RBF) neural networks has earned 54 citations, demonstrating his sustained focus on finite-time tracking performance and adaptive learning mechanisms. Earlier work exploring Adaptive Fuzzy PID Sliding Mode Controllers further reflects his commitment to bridging classical and intelligent control paradigms. Tran's research trajectory also extends into medical robotics, with investigations into optimizing the Iterative Closest Point (ICP) algorithm for surgical registration applications. Collectively, his contributions address the critical challenge of controlling complex robotic systems in real-world, uncertain environments, making his work highly relevant to both academic researchers and engineers advancing automation and surgical robotics technologies.