Papers
11
Total Citations
97
H-Index
7
About
Amar Rezoug is a control systems researcher whose work sits at the intersection of advanced nonlinear control theory, artificial intelligence, and robotics. Over more than a decade of prolific research, he has established himself as a specialist in sliding mode control — particularly nonsingular terminal sliding mode (NTSMC) frameworks — applied to challenging robotic platforms including pneumatic artificial muscle-driven manipulators, multi-degree-of-freedom robot arms, and unmanned aerial manipulators (UAMs). Rezoug's most impactful contributions include pioneering hybrid control architectures that fuse metaheuristic optimization — such as his extended grey wolf optimization algorithm — with adaptive super-twisting laws and time delay estimation, achieving fast convergence and robust disturbance rejection in uncertain robotic systems. His 2022 and 2019 works, accumulating 17 and 16 citations respectively, demonstrate sustained relevance in both ground and aerial robotics. Earlier contributions integrating radial basis function neural networks (RBFNN), Type-2 fuzzy logic, and sliding mode theory laid important groundwork for intelligent, uncertainty-tolerant controllers. With work spanning mobile robot navigation, pneumatic actuator control, and aerial manipulation, Rezoug's research portfolio reflects a consistent commitment to bridging theoretical control design with experimental validation, making him a notable figure in intelligent robotics control.
Research Focus
Key Achievements
Top Papers
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- 7Visual based lane following for non-holonomic mobile robot7 citations · 2009
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