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About

Yongling Xia is a rising leader in the control and robotics community, whose work focuses on enhancing the safety, precision, and resilience of robotic manipulators under real-world constraints. Her research centers on adaptive fault-tolerant control, disturbance rejection, and event-triggered strategies, addressing critical challenges in transient performance and system reliability. In her most cited work (2025, 21 citations), Xia introduced a finite-time adaptive fault-tolerant control framework that integrates backstepping with neural networks, ensuring guaranteed transient performance for uncertain robotic systems even under actuator faults. She further advanced the field with a disturbance observer-based full-state constrained controller (2023, 14 citations) that handles dead-zone nonlinearities—a common yet difficult practical issue. Her recent contributions include a novel fault-tolerant strategy using adaptive time-delay estimation (2025, 5 citations) and a prescribed-time adaptive event-triggered control method (2025, 3 citations) that reduces communication load while maintaining stability. Xia’s work is distinguished by its rigorous theoretical guarantees and direct applicability to industrial robotics, making her a key figure in the push toward more intelligent, fault-resilient autonomous systems.