Marzuki Khalid

Papers

Total Citations

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About

Marzuki Khalid is a researcher whose work centers on control systems, robotics, and machine learning, with a particular focus on enhancing the performance of electromechanical systems. His major contributions include advancing weighted kernel regression (WKR) techniques to address the small-sample learning problem, demonstrating their superiority over traditional artificial neural networks in robotic manipulator applications. Additionally, Khalid has made notable strides in control theory through Q-parameterization for DC motor speed control, a critical component in robotic systems. While his most-cited papers have accrued modest citation counts—3 and 2 citations respectively—their impact lies in laying foundational work for improving controller design and regression methods in robotics. Khalid’s research is characterized by a practical, application-driven approach, bridging theoretical advancements with real-world robotic and motor control challenges. His work serves as a stepping stone for students and researchers exploring efficient learning algorithms and robust control strategies in automation and robotics.