Papers
2
Total Citations
24
H-Index
2
About
Dr. Kamel Sabahi is a leading researcher in advanced control systems, specializing in type-2 fuzzy neural networks and predictive control for complex nonlinear systems. His work addresses critical challenges in controlling input-delay systems—those where time lags in feedback can destabilize performance—by developing intelligent, adaptive controllers that handle uncertainties and external disturbances with remarkable precision. His most influential paper, "Indirect predictive type-2 fuzzy neural network controller for a class of nonlinear input-delay systems" (2017, 15 citations), introduced a novel framework that fuses type-2 fuzzy logic with neural network prediction, enabling robust real-time control. Building on this, his 2019 study on feedback error learning-based type-2 fuzzy neural network predictive controllers (9 citations) further refined these methods, employing a predictor to estimate system states and mitigate delays. With a cumulative citation count exceeding 24, Dr. Sabahi’s work is widely recognized for bridging theoretical rigor and practical applicability, offering scalable solutions for robotics, industrial automation, and aerospace systems. His contributions empower engineers to design more reliable controllers for time-sensitive environments, marking him as a pivotal figure in modern nonlinear control theory.
Research Focus
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