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About

Lilia Sidhom is a robotics and control systems researcher whose work focuses on the intersection of advanced estimation, fault detection, and system identification for robotic manipulators. Her research primarily spans three interconnected areas: robust state estimation using sliding mode differentiators, data-driven fault detection and diagnosis, and online parametric identification for nonlinear systems. Sidhom’s most influential work, "Identification of a robot manipulator based on an adaptive higher order sliding modes differentiator" (11 citations), established a rigorous theoretical framework for adaptive state estimation, providing convergence proofs that underpin subsequent advances. She has since extended this foundation into practical fault detection, notably developing a Model-Free Fault Detection (MFFD) method inspired by Model-Free Control (5 citations) and a comparative study on intermittent fault detection for MIMO systems using SCARA robots (6 citations). Her 2021 work on software sensors for online parametric identification in closed-loop robotic systems (4 citations) demonstrates her commitment to real-time, deployable solutions. Sidhom’s contributions are particularly notable for bridging theoretical sliding mode control with practical industrial applications, offering robust methods that require minimal prior knowledge of system noise—a significant advantage for real-world deployment. Her research trajectory shows a clear progression from fundamental estimation theory to applied fault-tolerant control, making her work valuable for both academic researchers and automation engineers seeking reliable, data-driven approaches for smart manufacturing.