Yuzhu Xiang
Papers
2
Total Citations
10
H-Index
2
About
Yuzhu Xiang is a leading researcher in the control and coordination of complex multi-agent and robotic systems, with a focus on achieving high-performance, time-critical behaviors. Their work addresses fundamental challenges in heterogeneous multi-agent systems, where agents with different dynamics must achieve consensus. In a highly cited 2025 paper, Xiang introduced a distributed adaptive observer to solve finite-time consensus for nonholonomic chained-form systems—a critical problem for applications like smart IoT and autonomous vehicle platooning. This contribution has already garnered 7 citations, signaling its immediate impact. Xiang also pioneers the integration of reinforcement learning with fixed-time control theory. Their 2025 work on robotic manipulators proposes an RL-based, fixed-time prescribed performance control scheme, using a nonsingular fast terminal sliding surface to guarantee convergence within a bounded time, regardless of initial conditions. This approach elegantly handles unknown disturbances and model uncertainties, achieving 3 citations. By merging adaptive control, sliding mode techniques, and machine learning, Xiang is shaping the future of autonomous systems that must operate reliably under strict timing constraints, making their research essential for next-generation robotics and networked control.
Research Focus
Key Achievements
Top Papers
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