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Ahmad Azlan Mat Isa is a researcher specializing in robotics, control systems, and flexible structure dynamics. His work focuses on addressing fundamental challenges in under-actuated and flexible robotic systems, particularly in developing intelligent control strategies for manipulators. His most cited paper (7 citations) introduces a real-time position prediction algorithm for under-actuated robot manipulators using artificial neural networks (ANN), proposing an adaptive learning approach to predict passive joint positions and enhance system accuracy. In another key contribution (3 citations), he investigates control of flexible beams with unmodelled dynamics, applying second-order pole placement and LQR techniques to manage nonholonomic constraints in flexible-link manipulators. His research also extends to modal identification of flexible structures (2 citations), contributing to the broader understanding of vibration and control in robotic systems. Though his citation counts are modest, his work represents foundational steps in integrating neural network-based prediction with classical control methods, offering practical insights for researchers tackling the complexities of under-actuated and flexible robotics. His contributions are particularly relevant for advancing adaptive control in real-world robotic applications.