Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

Mr. Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

King Fahd University of Petroleum and Minerals | Saudi Arabia

Ibrahim Khalil Kabir is a control and robotics researcher working at the intersection of control theory and artificial intelligence, with a strong focus on learning-based robotics, socially aware navigation, and human–robot interaction. He holds an MSc in Systems and Control Engineering and a BEng in Mechatronics Engineering, with a solid academic record and advanced training in autonomous systems. His research experience spans graduate teaching and research assistantships, where he contributed to robot path planning, navigation, and hands-on laboratory instruction using real robotic platforms. His scholarly output includes peer-reviewed journal and conference publications covering UAV control, mobile robot navigation, deep reinforcement learning, and socially aware robotic systems. According to Google Scholar, his research profile reflects an emerging h-index supported by multiple indexed documents and a steadily growing citation count, indicating increasing impact in robotics and intelligent control research. His work has appeared in reputable venues such as IEEE Access, Machine Learning and Knowledge Extraction, and IEEE conferences. He has received several academic honors, including national merit scholarships and highest GPA awards. Overall, his research trajectory demonstrates a strong foundation and growing influence in intelligent robotics, positioning him well for advanced doctoral research in learning-enabled autonomous systems.

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Chih-Lyang Hwang | Electrical Engineering | Best Researcher Award

Prof. Chih-Lyang Hwang | Electrical Engineering | Best Researcher Award

National Taiwan University of Science and Technology | Taiwan

Dr. Chih-Lyang Hwang (SM’08) is a distinguished researcher and academic in the field of electrical and mechanical engineering, currently serving as a Research Fellow at the Intelligent Robot Center, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan. He earned his Ph.D. in Mechanical Engineering from Tatung Institute of Technology  and subsequently held professorial positions at Tatung Institute of Technology, Tamkang University, and NTUST. With an extensive academic career spanning over three decades, he has contributed significantly to robotics, fuzzy neural modeling, nonlinear control, and human–robot interaction. His research also encompasses distributed visual and wireless localization, UAV control, and emotion recognition. Dr. Hwang has been a Visiting Scholar at Georgia Institute of Technology and Auburn University, broadening his international academic collaborations. He has authored numerous influential journal and conference papers, amassing over 3,383 citations, 533 documents, and an H-index of 29. Recognized among the world’s top 2% scientists by Stanford University for multiple years, he has also received Excellent and Outstanding Research Awards from NTUST and 2024. His enduring contributions continue to advance intelligent robotics and control systems research globally.

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Featured Publications

Hwang, C.-L., Yang, C.-C., & Hung, J.-Y. (2017). Path tracking of an autonomous ground vehicle with different payloads by hierarchical improved fuzzy dynamic sliding-mode control. IEEE Transactions on Fuzzy Systems, 26(2), 899–914.

Hwang, C.-L., Jan, C., & Chen, Y.-H. (2001). Piezomechanics using intelligent variable-structure control. IEEE Transactions on Industrial Electronics, 48(1), 47–59.

Hwang, C.-L., Chang, L.-J., & Yu, Y.-S. (2007). Network-based fuzzy decentralized sliding-mode control for car-like mobile robots. IEEE Transactions on Industrial Electronics, 54(1), 574–585.

Hwang, C.-L., Chiang, C.-C., & Yeh, Y.-W. (2013). Adaptive fuzzy hierarchical sliding-mode control for the trajectory tracking of uncertain underactuated nonlinear dynamic systems. IEEE Transactions on Fuzzy Systems, 22(2), 286–299.

Hwang, C.-L. (2004). A novel Takagi–Sugeno-based robust adaptive fuzzy sliding-mode controller. IEEE Transactions on Fuzzy Systems, 12(5), 676–687