Sungpil Yang

The University of Texas at Austin

Papers

1

Total Citations

35

H-Index

1

About

Sungpil Yang is a control theorist whose research focuses on adaptive and nonlinear control for complex mechanical systems, with particular emphasis on Euler–Lagrange dynamics. His most cited work, "Dynamically Scaled Immersion and Invariance Adaptive Control for Euler–Lagrange Mechanical Systems" (2017, 35 citations), introduces a novel adaptive control framework that addresses fundamental challenges in rigid-body attitude control and multi-link robot manipulators. The key innovation lies in dynamically scaling the immersion and invariance approach to handle systems where model parameters are unknown, requiring only the lower bound of the inertia matrix's smallest eigenvalue—a significantly relaxed assumption compared to conventional methods. This contribution has proven valuable for applications requiring robust performance under parametric uncertainty, such as aerospace attitude control and industrial robotics. Yang’s work bridges theoretical rigor with practical implementability, offering engineers a powerful tool for designing adaptive controllers that maintain stability and performance without full system knowledge. His research continues to influence the development of intelligent control strategies for next-generation autonomous systems.

Research Focus

Key Achievements

1
H-Index
1
Papers
35
Total Citations
35
Avg Citations/Paper
🏆 Most Cited Paper
Dynamically Scaled Immersion and Invariance Adaptive Control for Euler–Lagrange Mechanical Systems
35 citations · 2017
📈 Most Prolific Year: 2017 (1 Papers)
🤝 Key Collaborators: 2
🏛 Institutions: The University of Texas at Austin

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
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