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About

Abdul Saleem is a robotics researcher focused on advancing autonomous navigation and control systems for wheeled mobile robots and robotic manipulators. His primary research areas include trajectory tracking control, neural network-based adaptive controllers, and real-time obstacle motion prediction for dynamic environments. Saleem’s most impactful work, “Neural Network-Based Adaptive Controller for Trajectory Tracking of Wheeled Mobile Robots” (2022), has garnered 70 citations and addresses critical limitations in classical control systems by compensating for parameter uncertainties and external disturbances. He has also contributed to the comparative analysis of tracking control algorithms (12 citations) and developed innovative approaches such as a neural network-based model reference controller for robot arm tracking and a TID controller for wheeled mobile robots. His work on real-time obstacle motion prediction using a neural network-based extended Kalman filter further demonstrates his commitment to solving complex navigation challenges in dynamic settings. With a growing citation record and a focus on practical, adaptive solutions, Saleem is establishing himself as a promising voice in intelligent robotics and control theory.