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About

Li-Jui Chang is a robotics and control systems researcher whose work centers on the autonomous navigation of mobile robotic platforms, particularly car-like mobile robots (CLMRs). His research integrates advanced control methodologies—including fuzzy logic, sliding-mode control, and neural adaptive techniques—with networked and intelligent space environments to enable robust trajectory tracking and obstacle avoidance. Chang's most influential contribution, "Network-Based Fuzzy Decentralized Sliding-Mode Control for Car-Like Mobile Robots" (2007), has garnered 121 citations and stands as a landmark study in applying decentralized control architectures to mobile robotics under network constraints. His parallel work on mixed H₂/H∞ decentralized control (47 citations) further demonstrated his ability to address real-world uncertainties and disturbances in robotic navigation within intelligent spaces. Expanding into Internet-enabled systems, his 2008 research on fuzzy-neural adaptive control for smart-space navigation (23 citations) highlighted a forward-thinking approach to remote and distributed robot guidance using camera-based sensing. Across his body of work, Chang has consistently bridged theoretical control design with practical implementation, making meaningful contributions to the fields of intelligent transportation systems, autonomous robotics, and networked control—offering valuable frameworks for researchers working at the intersection of AI, control theory, and mobile robotics.