Thao Tran Phuong
Papers
6
Total Citations
116
H-Index
5
About
Thao Tran Phuong is a leading researcher in advanced motion control and robotics, specializing in disturbance observer theory, Kalman filtering, and force sensing for industrial robots. Her work addresses the critical challenge of achieving high-speed, high-precision robot motion in the presence of dynamic and load torques. Phuong’s major contributions include the development of a Kalman-filter-based instantaneous state observer (ISOB) for robust load torque compensation, and a novel force control method using a spring ratio and instantaneous state observer, enabling stable force interaction with environments. She also pioneered FPGA-based wideband force sensing to enhance haptic feedback bandwidth, and introduced a variable noise-covariance Kalman filter to improve state estimation under varying conditions. Her most cited paper, “Disturbance Observer and Kalman Filter Based Motion Control Realization” (2017), has garnered 43 citations, reflecting the impact of her integrated control frameworks. With over 100 total citations across her key publications, Phuong’s research is foundational for next-generation industrial robotics, offering practical solutions for precise, robust, and responsive motion control in manufacturing and haptic applications.
Research Focus
Key Achievements
Top Papers
- 1Disturbance Observer and Kalman Filter Based Motion Control Realization43 citations · 2017
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