Phan Van Du

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About

Phan Van Du is a rising researcher in advanced robotics and intelligent control systems, with a focus on enhancing the precision and robustness of industrial manipulators. His most-cited work, "Adaptive Terminal Sliding Mode Control Using RBF Neural Network for Industrial Robot Manipulators" (2025), has already garnered 5 citations, signaling early impact in the field. In this paper, Du introduces a novel hybrid control strategy that integrates radial basis function (RBF) neural networks with terminal sliding mode control, addressing critical challenges in trajectory tracking and disturbance rejection for robotic arms. His contribution lies in developing an adaptive framework that improves convergence speed and reduces chattering—common limitations in traditional sliding mode approaches—making it highly applicable to real-world manufacturing and automation tasks. This work exemplifies his broader research interests in nonlinear control, neural network-based adaptive systems, and human-robot interaction. As an emerging scholar, Du’s innovative synthesis of machine learning and control theory positions him as a promising voice in next-generation robotics, with potential to influence both academic research and industrial practice.