Papers
3
Total Citations
27
H-Index
2
About
Jie Tao is a rising researcher in control theory and multi-agent systems, with a focus on resilient filtering and adaptive formation control under uncertainty. Their work addresses critical challenges in networked systems, particularly for Markov jump systems and heterogeneous multi-agent systems. Tao’s most-cited paper (2022, 21 citations) introduces an event-triggered resilient filtering approach using interval-type gain uncertainties and hidden Markov models to handle asynchronous constraints, advancing robust state estimation. More recently, Tao has pioneered adaptive event-triggered set-membership formation control for heterogeneous multi-agent systems, with applications to omnidirectional robots (2025, 4 citations). Their innovative “virtual neighbor framework” (2025, 2 citations) enables leader-following formation under time-varying topologies, improving adaptability to dynamic network changes. This work bridges theoretical control design and practical robotics, demonstrating impact in both foundational theory and real-world deployment. Tao’s contributions are particularly notable for addressing uncertainty and asynchronicity—key obstacles in modern cyber-physical systems—making their research highly relevant for students and engineers working on autonomous multi-robot coordination, networked control, and resilient system design.
Research Focus
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Top Papers
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