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About

Yana Yang is a robotics and control systems researcher whose work spans teleoperation, synchronization control, and adaptive neural network methodologies. Her research focuses on addressing critical challenges in master-slave robotic systems, particularly under real-world constraints such as communication delays, network uncertainty, and limited sensor feedback. Among her most recognized contributions is her 2022 work on adaptive neural network control for flexible telerobotic systems operating under communication constraints, which has garnered 9 citations and demonstrates her ability to bridge intelligent learning methods with practical robotic deployment. Her parallel investigation into fixed-time synchronization control for industrial master-slave systems reflects a commitment to improving transparency and reliability in human-robot interaction. Additionally, her predictor-based robust synchronization framework tackles the demanding problem of large networked communication time delays in non-collocated configurations — a scenario that traditional teleoperation research often overlooks. Earlier work from 2014 on image-based robotic control highlights her foundational interest in observer design, specifically applying Immersion and Invariance observers to estimate unavailable joint velocity signals without known camera parameters. Collectively, Yang's research offers meaningful advances in making robotic teleoperation systems more robust, adaptive, and deployable in complex industrial environments.