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About

Dr. Gwo-Ruey Yu is a leading researcher in intelligent control systems, with a primary focus on the advanced fuzzy control of robotic and mechatronic systems. His work is distinguished by the innovative integration of Takagi-Sugeno (T-S) fuzzy models with powerful optimization and stability techniques, including Linear Matrix Inequalities (LMI), Sum of Squares (SOS) programming, and evolutionary algorithms like Particle Swarm Optimization (PSO) and Quantum Evolutionary Algorithms. Dr. Yu’s major contributions lie in developing robust controllers for complex, underactuated robots—such as self-balancing two-wheeled robots and two-link robotic arms—where he has successfully addressed critical challenges in path tracking, stability, and disturbance rejection. His most cited work, a 2017 study on PSO-based fuzzy control for a self-balancing robot, has garnered 9 citations, demonstrating its impact on the field. Through a prolific series of publications spanning from 2009 to 2019, Dr. Yu has consistently advanced the state of the art, creating more efficient and reliable control architectures that reduce computational complexity while enhancing performance. His research provides a vital bridge between theoretical control theory and practical robotic applications, making him a key figure for students and researchers exploring intelligent automation.