Mulugeta Haile

Papers

Total Citations

140

H-Index

About

Mulugeta Haile is a researcher whose work sits at the dynamic intersection of robust state estimation, nonlinear control systems, and machine learning-based control design. His most influential contribution, "Robust Extended Kalman Filtering for Systems With Measurement Outliers," addresses a critical vulnerability in standard filtering methods — their susceptibility to corrupted measurements arising from sensor failures, environmental disturbances, and cyberattacks. Published across two iterations (2019 and 2021), this work has collectively accumulated over 70 citations, demonstrating its broad relevance to engineers designing reliable systems in adversarial or noisy environments. Haile's 2023 paper, "Model-free Tracking Control of Complex Dynamical Trajectories with Machine Learning," represents a significant leap forward in control engineering. By eliminating the requirement for explicit system model knowledge — a longstanding bottleneck in trajectory tracking for robotics and autonomous systems — this work opens new possibilities for civil and defense applications, earning 67 citations in just two years. Together, his contributions reflect a coherent research vision: making dynamic systems more resilient and adaptable, whether through better filtering under uncertainty or through data-driven control strategies that sidestep traditional modeling constraints.