Atef Khedher

Papers

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About

Dr. Atef Khedher is a researcher specializing in nonlinear systems control and fault diagnosis, with a particular focus on Takagi-Sugeno (T-S) fuzzy modeling. His work centers on developing advanced observer designs—such as proportional multiple integral observers and unknown input observers—to simultaneously estimate system states and detect actuator or sensor faults in complex nonlinear systems. In his most cited work (2021, 7 citations), Dr. Khedher applied an artificial neural network with unknown inputs to track the trajectory and estimate faults of the MIABOT robot, demonstrating a practical integration of neural networks with T-S model-based control. His earlier contributions (2015, 2 citations; 2019, 2 citations) established foundational observer frameworks for state and fault estimation, using mathematical transformations to create augmented systems that unify fault detection and state tracking. While his citation counts are modest, Dr. Khedher’s work represents a focused contribution to the robust control and fault-tolerant systems community, offering practical tools for real-world applications in robotics and automation. His research is particularly valuable for engineers seeking to enhance the reliability and safety of autonomous systems through intelligent fault estimation techniques.