Dur‐e‐Zehra Baig

Ghulam Ishaq Khan Institute of Engineering Sciences and Technology

Papers

Total Citations

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About

Dur-e-Zehra Baig is a researcher advancing the field of robotics and nonlinear system identification, with a focus on two-wheeled robots (TWRs). Her work centers on developing data-driven approaches to model and control complex, nonlinear robotic systems, bridging the gap between theoretical kinematics and practical implementation. Baig’s major contributions include pioneering the use of feed-forward neural networks as kinematic estimators, enabling efficient prediction of a TWR’s movement in x and y directions and rotational angles. In her most-cited paper (17 citations, 2022), she introduced a dynamic modeling framework that leverages data-driven techniques to capture the fundamental nonlinear kinematics of TWRs, validated through Simulink simulations across diverse operating conditions. This work has significant implications for autonomous robotics, offering scalable solutions for real-time control and system identification without relying on complex analytical models. Her second notable paper (5 citations, 2022) further refined this approach by employing artificial neural networks for enhanced estimation accuracy. Baig’s research is impactful for students and engineers interested in intelligent control systems, machine learning in robotics, and data-efficient modeling—showcasing how neural networks can transform the design of agile, self-balancing robots.