Papers
5
Total Citations
58
H-Index
4
About
Kevin Guelton is a leading researcher in nonlinear control systems, with a primary focus on Takagi-Sugeno (T-S) fuzzy descriptor models and their application to robotic and rehabilitation systems. His major contributions lie in advancing the theoretical framework for T-S fuzzy systems, particularly through the development of non-PDC (Parallel Distributed Compensation) controller designs using line-integral Lyapunov functions—a significant step beyond earlier BMI-based approaches. Guelton has also pioneered descriptor modeling techniques for complex mechanical systems, such as two-link robot manipulators and pneumatic robots, enabling more robust control under external disturbances. His work on LMI-based H∞ controller design for uncertain T-S descriptors has provided powerful tools for ensuring stability and performance in real-world applications. With over 18 citations for his foundational 2006 paper on robot manipulator modeling, Guelton’s research has had a lasting impact on both theoretical control engineering and practical implementations. Notably, his applied work includes trajectory generation for lower-limb rehabilitation devices like Sys-Reeduc, where he integrates user intention into safe control structures, bridging the gap between advanced control theory and assistive medical technology.
Research Focus
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