Muhammad Sohaib

Lahore Garrison University, University of Ulsan

Papers

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About

Muhammad Sohaib is a researcher specializing in fault diagnosis and fault-tolerant control of robotic systems, with a particular focus on robot manipulators. His work addresses critical challenges in ensuring the reliability and safety of multi-degree-of-freedom nonlinear systems used in medical and automotive applications. Sohaib’s most cited paper, "An SVM-Based Neural Adaptive Variable Structure Observer for Fault Diagnosis and Fault-Tolerant Control of a Robot Manipulator" (2020, 29 citations), introduces an innovative hybrid approach combining support vector machines with neural adaptive observers to detect and mitigate faults under uncertain operating conditions. This contribution is notable for enhancing the robustness of fault diagnosis in complex robotic environments. His earlier work, "Fault Diagnosis of a Robot Manipulator Based on an ARX-Laguerre Fuzzy PID Observer" (2018, 6 citations), further demonstrates his expertise in developing adaptive observer-based methods using fuzzy logic and system identification techniques. Through these studies, Sohaib has advanced the field of intelligent control and fault tolerance, providing practical solutions for improving the operational integrity of robotic manipulators in demanding real-world scenarios.