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About

Dejie Yu is a leading researcher in robust control theory, with a focus on uncertain dynamical systems, fuzzy logic, and underactuated robotics. His work bridges game-theoretic frameworks and adaptive control to address complex challenges in system stability and performance. Yu’s major contributions include pioneering the use of Nash-game and Stackelberg-theoretic approaches for optimal control design in fuzzy dynamical systems, where uncertainty is time-varying and bounded within fuzzy sets. This dual-game strategy has provided novel methods for enhancing robustness and performance simultaneously. His impact is evidenced by citation counts of 39 and 37 for his foundational papers on game-oriented control, which are widely referenced in the field. Additionally, Yu has advanced practical applications through constraint-following control for underactuated systems, such as a two-wheeled mobile robot, and adaptive robust control for soft robotic snakes using a smooth-zone approach, each garnering over 35 citations. These achievements highlight his ability to translate theoretical innovations into real-world robotic solutions, making him a notable figure in control engineering and robotics.