M. Tadjine
Papers
2
Total Citations
12
H-Index
2
About
M. Tadjine’s research lies at the intersection of robotics, nonlinear control theory, and artificial intelligence, with a focus on the modeling and robust control of flexible-link robotic manipulators. His work addresses the fundamental challenge of achieving precise motion control in complex, underactuated systems. A key contribution is his comprehensive mathematical model of a transversely vibrating flexible-link robot arm carrying a tip payload (2007, 7 citations), which provides a rigorous foundation for analyzing and controlling such systems. Building on this, Tadjine pioneered the hybridization of sliding mode control with neural networks, as demonstrated in his work on RBFNN-HOMS nonsingular terminal sliding control for n-DOF manipulators (2016, 5 citations). This approach combines the robustness of higher-order sliding modes with the adaptive learning capabilities of radial basis function neural networks, effectively mitigating chattering and singularities. His work is notable for advancing a relatively new paradigm in control engineering—the fusion of artificial intelligence with classical sliding mode techniques—offering a pathway toward more intelligent, resilient robotic systems. With a career spanning foundational modeling and cutting-edge intelligent control, Tadjine’s research continues to influence the design of high-performance robotic manipulators.
Research Focus
Key Achievements
Top Papers
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