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About

Yanling Yin is a leading researcher in the field of intelligent control systems, with a primary focus on model-free adaptive control, iterative learning control, and the security of networked nonlinear systems. Her work addresses critical challenges in robotics and multi-agent coordination, particularly in environments affected by communication constraints and cyber threats. A major contribution is her pioneering application of Model Free Adaptive Control (MFAC) to robotic manipulator trajectory tracking, a data-driven approach that eliminates the need for complex system models, as detailed in her most-cited paper (17 citations). Dr. Yin has also made significant strides in resilient control, developing an iterative learning control framework that ensures system stability under hybrid cyberattacks, including denial-of-service and false data injection (9 citations). Her research extends to distributed formation control for nonholonomic wheeled mobile robots, where she has designed robust protocols that account for channel noise and switching topologies (6 citations). Through these innovative contributions, Yanling Yin is advancing the safety, autonomy, and reliability of modern robotic and networked control systems.