Shih-Ting Chen

Papers

Total Citations

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About

Shih-Ting Chen is a leading researcher in intelligent robotics and industrial cyber-physical systems (ICPS), with a focus on advanced control strategies for autonomous multi-robot coordination. Their most-cited work introduces an innovative collision-free formation control method for ball-riding robots, integrating an output recurrent broad learning strategy (ORBLS) with backstepping sliding mode formation control (BSMFC). This contribution addresses critical challenges in dynamic, cyber-physical environments, enabling robots to maintain stable formations while avoiding collisions—a key requirement for smart manufacturing and autonomous logistics. With 4 citations since 2024, this paper has already garnered attention for its practical approach to bridging machine learning and real-time control. Chen’s research stands out for its application of broad learning networks, which offer efficient, scalable alternatives to deep learning, and for its emphasis on cyber-physical security and robustness. By tackling the intersection of artificial intelligence, control theory, and industrial automation, Chen is shaping the future of intelligent, cooperative robotics in complex, real-world systems. Their work is essential reading for researchers exploring adaptive, decentralized control in next-generation industrial ecosystems.