Papers
2
Total Citations
23
H-Index
2
About
Yudi Wang’s research lies at the intersection of intelligent manufacturing and secure cyber-physical systems, with key contributions in robotic machining and nonlinear networked control. In a pioneering 2022 study on spatial path planning for robotic milling of automotive casting components, Wang developed a method based on optimal machining posture that significantly reduces human involvement while boosting production efficiency and quality—work that has garnered 12 citations and practical relevance in smart factories. More recently, Wang has tackled the critical challenge of cybersecurity in control systems, proposing an observer-based adaptive neural network tracking control for nonlinear networked systems under intermittent denial-of-service attacks. This 2025 paper, with 11 citations, introduces a finite-time prescribed performance method that ensures system stability and tracking accuracy even when attackers disrupt communication—a vital advance for safety-critical applications. Wang’s ability to bridge physical automation and resilient control demonstrates a rare versatility, earning recognition for addressing both industrial productivity and emerging cyber threats. These contributions position Wang as a rising figure in advanced manufacturing and secure control theory, with work that directly impacts real-world automotive production and networked system reliability.
Research Focus
Key Achievements
Top Papers
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