Xiuming Yao
Papers
6
Total Citations
144
H-Index
5
About
Xiuming Yao is a leading researcher in advanced control systems, specializing in disturbance rejection, robust model predictive control, and trajectory optimization for robotic manipulators. Their work addresses critical challenges in uncertain and constrained environments, particularly for semi-Markov jump systems and nonlinear non-strict feedback systems. Yao’s most cited paper (2021, 66 citations) introduces a composite refined anti-disturbance control strategy for a 2-degree-of-freedom robot arm, effectively handling harmonic and energy-bounded disturbances. Another key contribution (2023, 30 citations) proposes a hierarchical framework combining computed-torque-like control with event-triggered robust model predictive control, enhancing tracking performance under bounded disturbances. Yao also developed time-optimal trajectory optimization methods using improved particle swarm optimization (2022, 26 citations), balancing kinematic and dynamic limits for serial manipulators. Recent work (2024, 7 citations) presents an innovative adaptive learning disturbance observer for asymptotic tracking in non-strict feedback systems. With over 140 total citations, Yao’s research is pivotal for advancing autonomous robotic systems, offering practical solutions for real-world applications like industrial automation and precision control.
Research Focus
Key Achievements
Top Papers
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