Xiaojiang Mu

Beijing University of Technology, Shenzhen Institute of Information Technology

Papers

Total Citations

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About

Xiaojiang Mu is a robotics and control systems researcher whose work centers on intelligent control methodologies for complex robotic systems. Specializing in the intersection of advanced control theory and artificial intelligence, Mu has made consistent contributions to the field of trajectory tracking control for multi-link robotic manipulators — systems notoriously difficult to govern due to model uncertainties and unpredictable external disturbances. Mu's most recognized contribution, a neural sliding mode controller for multi-link robots (2008, 7 citations), introduced a novel global sliding mode manifold that eliminates the reaching phase inherent in conventional sliding mode control, improving system robustness from the outset of motion. Building on this foundation, Mu expanded the framework by incorporating fuzzy logic and genetic algorithms, producing a hybrid FNSMCGA approach (2010, 5 citations) that leverages evolutionary optimization to enhance controller performance. His 2012 publications further refined these ideas through both direct and indirect adaptive fuzzy sliding mode strategies, demonstrating a progressive research trajectory aimed at handling real-world model uncertainty more elegantly. Collectively, Mu's body of work reflects a thoughtful evolution from neural to fuzzy-adaptive intelligent control architectures, offering practical tools for engineers designing reliable, disturbance-resilient robotic systems.