Papers
2
Total Citations
9
H-Index
2
About
Shih-Chien Tang is a pioneering researcher in robotics and control systems, with a primary focus on advancing motion control for robot manipulators. His work bridges classical control theory with modern deep learning techniques, particularly through the integration of convolutional neural networks (CNNs) into resolved acceleration control schemes. In his most-cited paper (2021, 7 citations), Tang introduced a deep regression-based CNN approach that enhances the precision of end-effector acceleration regulation, offering a novel pathway for achieving robust, real-time motion control in complex robotic systems. Earlier, his foundational 1998 work on robust controller design for electrically-driven rigid-body robots (2 citations) addressed critical challenges in industrial robotics, including the elimination of joint velocity measurements and the management of parameter estimation uncertainties. This research laid important groundwork for more reliable and cost-effective robotic implementations. Tang’s contributions are particularly valuable for students and researchers exploring the intersection of neural networks and classical control, demonstrating how deep learning can augment traditional robotic control architectures to achieve greater accuracy and adaptability in dynamic environments.
Research Focus
Key Achievements
Top Papers
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