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About

Anantasak Wongkamhang is a rising figure in intelligent control systems, whose work bridges fuzzy logic, expert systems, and robotic manipulation. His primary research areas include fuzzy control optimization, hybrid intelligent systems, and the application of advanced algorithms to robotic manipulators and assistive technologies. His most influential work, "Optimizing Membership Function Tuning for Fuzzy Control of Robotic Manipulators Using PID-Driven Data Techniques" (2023, 26 citations), introduces a data-driven approach to automating the design of membership functions—traditionally a manual, expertise-dependent process—significantly improving control precision and adaptability. He has also pioneered hybrid fuzzy-expert systems for robotic manipulators, dynamically switching between inference methods to enhance decision-making. In a notable comparative study, he evaluated Takagi-Sugeno-Kang and Mamdani algorithms in Type-1 and Interval Type-2 fuzzy controllers for self-balancing wheelchairs, contributing to safer, more responsive mobility aids. His ongoing work on membership function analysis for 3-axis manipulators further underscores his commitment to refining control in industrial and medical contexts. With a growing citation record and a focus on practical, real-world applications, Wongkamhang is establishing himself as a key contributor to the next generation of intelligent robotic control.