Aissa Bencherif
Papers
6
Total Citations
73
H-Index
4
About
Aissa Bencherif is a leading researcher in intelligent control systems, with a primary focus on the trajectory tracking of mobile robots and robotic arm manipulators. His work masterfully integrates fuzzy logic, neural networks, and adaptive sliding mode control to solve complex, real-world navigation and manipulation challenges. A key contribution is his development of a robust adaptive sliding mode controller using stochastic gradient descent (ASMCSGD), which significantly improves chattering elimination and tracking precision for robot arms (2024, 17 citations). He has also pioneered an indirect adaptive control PID strategy using neural networks and a discrete extended Kalman filter for wheeled mobile robots (2024, 13 citations), and a recurrent TSK interval type-2 fuzzy neural network for mobile robot control (2019, 28 citations). His earlier work on data fusion for robot navigation using fuzzy logic (2017, 9 citations) laid the groundwork for these advanced systems. With a career spanning from foundational ADALINE neural network controllers (2015) to cutting-edge adaptive strategies with forgetting factors (2025), Bencherif’s research consistently pushes the boundaries of autonomous system performance, demonstrating profound impact in both theoretical development and practical application.
Research Focus
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Top Papers
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