Liangliang Yang

Zhejiang Sci-Tech University, Kitami Institute of Technology

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About

Liangliang Yang is a researcher whose work spans the intersection of intelligent control systems, rehabilitation robotics, and computer vision-driven agricultural automation. With expertise in advanced control methodologies and machine learning applications, Yang has made meaningful contributions to two distinct yet technologically interconnected domains. In the field of rehabilitation engineering, Yang's 2015 work on fuzzy neural network (FNN) control of pneumatic muscle-driven robotic arms addressed one of the most persistent challenges in assistive robotics: managing the high-order, time-delayed, and highly nonlinear behavior of soft actuator systems. By demonstrating that FNN controllers could outperform classical PID approaches in such complex environments, this paper garnered 29 citations and established Yang as a contributor to intelligent rehabilitation device development. More recently, Yang turned attention to precision agriculture, developing the Mask Positioner algorithm in 2023 — an innovative segmentation approach designed to accurately detect green fruit in visually cluttered orchard environments, supporting the advancement of autonomous harvesting robotics. This work reflects a broader research trajectory focused on applying intelligent algorithms to real-world robotic challenges. Yang's portfolio demonstrates a consistent commitment to bridging theoretical control design with practical, human-centered and agricultural applications.