Soroush Arabshahi
Papers
1
Total Citations
2
H-Index
1
About
Soroush Arabshahi is a robotics researcher whose work focuses on bridging the gap between theoretical control systems and practical, low-cost hardware implementation. His key contributions lie in robust control, neural network-based uncertainty compensation, and mobile robotics. His most-cited paper, "Robust Control of a Low-Cost Mobile Robot Using a Neural Network Uncertainty Compensator" (2014), presents an innovative approach that uses a radial basis function neural network (RBFNN) to estimate and counteract modeling uncertainties and external disturbances in real-time. This work demonstrates how advanced neural network techniques can make low-cost robotic platforms more reliable and capable in unstructured environments. While his citation count is modest, the practical significance of his research is notable for its potential to democratize robust robotics—making sophisticated control accessible to budget-constrained projects. Arabshahi’s contributions are particularly valuable for students and researchers interested in the intersection of machine learning and control theory, offering a clear example of how neural networks can enhance system performance without requiring expensive hardware.
Research Focus
Key Achievements
Top Papers
- 1