Max Robotics — Global Service Robot Directory + US Certification

About

Yi‐Jen Mon’s research lies at the intersection of intelligent control, fuzzy systems, and robotics, with a sustained focus on developing adaptive, hybrid controllers for autonomous mobile robots and nonlinear dynamic systems. His most-cited work, “Hybrid adaptive fuzzy controllers with application to robotic systems” (2003, 23 citations), established a foundational approach that blends fuzzy logic with adaptive mechanisms to improve robotic performance in uncertain environments. Mon further advanced the field with his development of Recurrent Fuzzy Neural Network (RFNN) control for MIMO nonlinear systems (2008, 11 citations), a method that integrates temporal dynamics into neural-fuzzy architectures for superior real-time control. His empirical work on mobile robot navigation is particularly notable: the Supervisory Adaptive Network-based Fuzzy Inference System (SANFIS) design (2012, 10 citations) and the image processing-based obstacle avoidance system using RFNN (2014, 9 citations) demonstrate practical, vision-driven solutions for autonomous path control. Later contributions, including machine vision-based sliding fuzzy-PDC control (2015, 7 citations), extend these ideas to service robot platforms. Across a career spanning two decades, Mon’s work has earned over 70 citations, reflecting its enduring relevance for researchers in intelligent robotics and adaptive control systems.