Junqi Yang
Papers
2
Total Citations
164
H-Index
2
About
Junqi Yang is a leading researcher in the field of advanced control systems, with a primary focus on data-driven iterative learning control and distributed multi-agent coordination. His work addresses critical challenges in nonlinear systems operating under real-world constraints, such as communication degradation and actuator limitations. Yang’s most influential contribution, an event-triggered model-free adaptive iterative learning control (MFAILC) framework for nonlinear systems over fading channels (2021, 120 citations), provides a robust solution for maintaining system stability and performance when output signals are corrupted by stochastic fading—a common issue in wireless networked control. This work is highly cited for its practical relevance to industrial automation and cyber-physical systems. Additionally, Yang has advanced distributed formation control for non-holonomic wheeled mobile robots, integrating velocity constraints with improved data-driven iterative learning (2020, 44 citations). His research bridges theoretical rigor with application, offering scalable, communication-efficient algorithms for autonomous robot swarms. Yang’s achievements are recognized for pioneering adaptive control methods that operate without explicit system models, making his work foundational for next-generation intelligent manufacturing and autonomous vehicle coordination.
Research Focus
Key Achievements
Top Papers
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