Homero Fonseca
Papers
1
Total Citations
10
H-Index
1
About
Homero Fonseca is a researcher in robotics and control systems, with a focus on adaptive neural control for mobile robots operating under uncertainty. His most-cited work, "Trajectory tracking of a wheeled mobile robot with uncertainties and disturbances: proposed adaptive neural control" (2015, 10 citations), addresses a fundamental challenge in autonomous navigation: maintaining precise trajectory tracking when both kinematic and dynamic models are affected by unknown disturbances. Fonseca’s key contribution is the integration of a kinematic neural controller (KNC) and a torque neural controller (TNC), creating a robust two-layer framework that adapts in real time without requiring precise system models. This approach is particularly valuable for real-world applications where wheeled robots face uneven terrain, variable loads, or sensor noise. While his citation count reflects a focused, specialized impact, the work demonstrates a rigorous methodology for combining neural networks with classical control theory—a bridge that remains relevant for researchers developing resilient autonomous systems. Fonseca’s research offers a practical template for engineers seeking to deploy adaptive controllers in unstructured environments, and his paper continues to inform studies on disturbance rejection and learning-based control in mobile robotics.
Research Focus
Key Achievements
Top Papers
- 1