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About

Najva Hassan is a robotics and control systems researcher whose work centers on autonomous mobile robot navigation, adaptive control, and intelligent systems. Her most influential contribution, "Neural Network-Based Adaptive Controller for Trajectory Tracking of Wheeled Mobile Robots" (2022), has garnered 70 citations and addresses a critical limitation of classical control systems — their inability to compensate for parameter uncertainties and external disturbances. By integrating neural networks into adaptive control frameworks, Hassan has advanced the robustness and reliability of trajectory tracking in wheeled mobile robots. Her complementary 2021 paper analyzing trajectory tracking control algorithms provides a foundational survey of performance trade-offs across different approaches, earning 12 citations and serving as a valuable reference for researchers entering the field. Hassan has also explored neural network-based TID controllers and real-time obstacle motion prediction using neural network-enhanced extended Kalman filters, reflecting her broader interest in applying deep learning to dynamic, uncertain environments. Collectively, her research bridges theoretical control design and practical autonomous navigation, making meaningful contributions to the development of intelligent, resilient mobile robotic systems capable of operating effectively in complex real-world conditions.