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About

Tasawan Puttasakul is a researcher at the forefront of intelligent robotic control and computer vision, with a focus on enhancing automation in industrial and biomedical applications. Her work bridges the gap between classical control theory and modern machine learning, developing hybrid systems that improve precision and adaptability. Puttasakul’s most cited paper (13 citations) introduces a ResNet18-based image segmentation method for single and dual object detection in robotic vision, demonstrating how deep learning can extract objects from backgrounds with high accuracy for industrial automation. She has also pioneered a hybrid fuzzy-expert system for robotic manipulator control, combining fuzzy logic’s flexibility with expert systems’ analytical rigor to dynamically optimize performance. Her comparative analysis of fuzzy membership functions—Gaussian, bell, triangular, and trapezoidal—provides a systematic framework for selecting the best controller for step and smooth input tracking. Additionally, Puttasakul has advanced servo motor precision by developing a PSO-tuned PID with feedforward control for MG996R motors, addressing the challenge of feedbackless actuators common in biomedical robotics. Her work is notable for its practical, application-driven approach, directly targeting real-world constraints in factory and clinical settings. With a growing citation impact, Puttasakul is establishing herself as a key contributor to intelligent automation and soft computing for robotics.