Thanana Nuchkrua

Research Scientist

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    Personal profile


    Dr.Thanana has joined the Control and Robotics group, Faculty of Mechanical Engineering (Robotic Track) of GTIIT in November 2020. Prior to this, he was a visiting scholar in Huazhong University of Science and Technology, China. He held postdoctoral position at advanced institute manufacturing with high-tech innovations (AIM-HI) in National Chung Cheng University, Taiwan. He achieved his Ph.D. degree with outstanding thesis from Sirindhorn International Institute of Technology, in 2015. He received Master degree with excellent thesis from Asian Institute of Technology. In year of 2019, he won the best paper award at the First International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-Symp) 2019 and won the best young paper award finalists at the SICE Annual Conference. He was session chair of “Model Learning for Control” in 14th IEEE conference on automation science and engineering (CASE) 2018, Munich, Germany and “Robotic and Automation Systems” in The SICE Annual Conference (SICE) 2019, Hiroshima, Japan. Since year of 2013-now, he has published paper in leading journals including Journal of Bionic Engineering, IET Control theory & Applications, International Journal of Control and Automation Systems, International Journal of Fuzzy Systems, International Journal of Advanced Robotics Systems, as well as a premier conference, IEEE/ASME conference on Advanced Intelligent Mechatronics (AIM), IEEE Conference on Automation Science Engineering (CASE), Asian Control Conference (ASCC), SICE Annual conference. In 2017 his paper was most highly cited papers in journal of Bionic Engineering. He has now acted as reviewer board of Journal of Machine.

    Detail information of my research can be found at 

    At GTIIT, his research interests include the control problem subjected to both natural and man-made systems where the foundations of systems & control enable improved performance and robustness. In addition, the unification of control theory tool with machine learning has also been on his research track where it is applying to a discipline in engineering science (i.e., bionic, robotics, power, autonomous systems, and system biology) with understanding the underlying physical phenomena.

    Research Interests

    Learning, Control, and Optimization with applications to engineering sciences:

    • Robotics
    • Pneumatic artificial muscle
    • Power system
    • Self driving car

    Research Group


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