Safeer Ullah
Papers
1
Total Citations
60
H-Index
1
About
Safeer Ullah is a leading researcher in advanced robotics and nonlinear control systems, with a particular focus on enhancing the precision and robustness of anthropomorphic manipulators. His most-cited work, "Adaptive FIT-SMC Approach for an Anthropomorphic Manipulator With Robust Exact Differentiator and Neural Network-Based Friction Compensation" (2022, 60 citations), addresses critical challenges in trajectory tracking for robotic arms. In this study, Ullah introduces a novel adaptive fast integral terminal sliding mode control (FIT-SMC) strategy, integrating a robust exact differentiator and neural network-based friction compensation to overcome parametric and model uncertainties. This approach achieves fast finite-time convergence, significantly improving control accuracy and stability in complex, nonlinear environments. His contributions are pivotal for advancing real-world robotic applications, from manufacturing to assistive technologies, where reliable and adaptive control is essential. With his work gaining traction in the control systems community, Ullah is recognized for bridging theoretical rigor and practical implementation, making him a notable figure in modern robotics and automation research.
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- 1