Papers
3
Total Citations
47
H-Index
2
About
Dr. Zeyang Yin is a leading researcher in nonlinear control theory and intelligent robotics, whose work bridges the gap between theoretical robustness and practical industrial automation. His most influential contribution, the 2019 paper on "Quasi fixed-time fault-tolerant control for nonlinear mechanical systems with enhanced performance" (43 citations), introduced a groundbreaking framework that guarantees system stability and performance within a bounded time, even under actuator failures—a critical advancement for safety-critical applications like autonomous vehicles and surgical robots. More recently, Dr. Yin has focused on motion planning for industrial manipulators, developing an optimal hierarchical planner (2024, 3 citations) that efficiently generates high-quality trajectories under complex constraints, significantly reducing computational overhead. He further advanced deep reinforcement learning for robotics with a Phased Reward Configuration Mechanism (2024, 1 citation), which systematically addresses the inefficiency and randomness of DRL in path planning by structuring the learning process into distinct, goal-oriented phases. Dr. Yin’s work is characterized by its dual emphasis on theoretical rigor and practical deployability, making him a pivotal figure in the evolution of fault-tolerant, high-performance robotic systems.
Research Focus
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