Wankun Sirichotiyakul
Papers
4
Total Citations
20
H-Index
3
About
Wankun Sirichotiyakul is a rising researcher in the field of nonlinear control theory, with a focus on underactuated robotic systems. His work centers on advancing passivity-based control (PBC) and energy-shaping methods, tackling the fundamental challenge of stabilizing systems with fewer actuators than degrees of freedom. Sirichotiyakul’s major contributions lie in developing data-driven frameworks that bridge classical control theory with modern machine learning. His most cited paper (2022, 9 citations) introduces a data-driven approach to interconnection and damping assignment PBC, eliminating the need to solve complex partial differential equations. He further extends this work with neural approximators and Bayesian inference (2022, 3 citations) for robust controller synthesis. Notably, his 2020 paper (5 citations) provides a novel framework using sum-of-squares programming to efficiently compute singularity-free workspaces for robotic manipulators—a practical tool for safe trajectory planning. Sirichotiyakul’s research is distinguished by its elegant integration of theoretical rigor with computational methods, offering scalable solutions for swing-up control and stabilization of underactuated robots. His growing citation record reflects the increasing relevance of his work to both academic control theory and real-world robotic applications.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4