Chenghong Zhang
Papers
2
Total Citations
29
H-Index
2
About
Chenghong Zhang is a control systems researcher whose work centers on iterative learning control (ILC) and its application to complex robotic systems. His research focuses on developing advanced control algorithms that enable robots to improve their performance over repeated task executions, with particular emphasis on systems characterized by nonlinear, time-varying, and strongly coupled dynamics. Zhang's most notable contributions involve the design of open-closed-loop iterative learning control schemes for challenging robotic platforms. His 2019 work on multiple flexible manipulator systems introduced a variable stiffness ILC method that achieves consensus tracking across networked robotic arms, effectively mitigating motion disturbances in repeatable task scenarios — a paper that has garnered 19 citations. Complementing this, his research on two-wheeled self-balancing mobile robots introduced a novel PD-based ILC algorithm incorporating a variable forgetting factor, addressing the formidable control challenges posed by inherently unstable, nonlinear platforms, accumulating 10 citations. Together, these contributions reflect Zhang's commitment to bridging theoretical control design with practical robotic applications. His work provides meaningful tools for engineers tackling trajectory tracking problems in next-generation autonomous and collaborative robotic systems, establishing him as a focused contributor to the intelligent control engineering community.
Research Focus
Key Achievements
Top Papers
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