Mia Loccufier
Papers
2
Total Citations
6
H-Index
2
About
Mia Loccufier is a researcher at the intersection of nonlinear dynamics, system identification, and experimental robotics. Her work focuses on bridging the gap between theoretical control strategies and real-world mechanical validation. She is perhaps best known for developing an evolutionary-based sparse regression algorithm to identify Coulomb friction terms in Duffing oscillators—a critical step toward accurately modeling nonlinear systems from experimental data. This work, which has garnered 4 citations, demonstrates her commitment to pushing system identification beyond pure simulation. Loccufier also tackles the practical challenges of underactuated control, as seen in her 2023 study on low-cost vision-based embedded control of a 2DOF robotic manipulator. By moving validation from simulation to physical hardware, she addresses a key bottleneck in robotics research. Her contributions are particularly valuable for students and engineers seeking to implement robust control on resource-constrained platforms. Through her focus on experimental identification and low-cost embedded systems, Loccufier is helping to make advanced nonlinear control more accessible and empirically grounded.
Research Focus
Key Achievements
Top Papers
- 1
- 2Low-cost vision-based embedded control of a 2DOF robotic manipulator2 citations · 2023