Papers
2
Total Citations
28
H-Index
2
About
Sergio Lucia is a leading researcher in control theory and robotics, with key contributions to inertial motion tracking and robust predictive control. His work addresses critical challenges in real-world systems, particularly the unreliability of magnetometer readings in magnetic environments. In his highly cited 2020 paper, "Sparse Magnetometer-free Inertial Motion Tracking – A Condition for Observability in Double Hinge Joint Systems" (18 citations), Lucia introduced a novel observability condition enabling accurate motion tracking of kinematic chains—such as human limbs or robotic actuators—without magnetometers, overcoming magnetic disturbances that degrade performance. This breakthrough has significant implications for exoskeletons, human motion analysis, and robotics. Earlier, his 2016 paper "Exploiting models of different granularity in robust predictive control" (10 citations) proposed a computationally efficient approach that uses detailed models only over short horizons, reducing complexity while maintaining robustness under uncertainty. Lucia’s work bridges theoretical rigor and practical application, offering scalable solutions for dynamic systems. His research continues to influence fields from wearable robotics to autonomous control, demonstrating a profound impact on both academic understanding and engineering practice.
Research Focus
Key Achievements
Top Papers
- 1
- 2Exploiting models of different granularity in robust predictive control10 citations · 2016