Carlos Aguilar-Avelar
Instituto Politécnico Nacional, Universidad Autónoma de Baja California
Papers
5
Total Citations
52
H-Index
4
About
Carlos Aguilar-Avelar is a leading researcher in robotics and control systems, whose work bridges theoretical rigor and practical engineering education. His primary contributions lie in trajectory tracking control, adaptive neural networks, and system identification for robotic manipulators and self-balancing robots. His most cited paper, "A MATLAB-based identification procedure applied to a two-degrees-of-freedom robot manipulator for engineering students" (2017, 22 citations), provides a formalized, accessible framework for parameter identification, directly addressing experimental disturbances and empowering undergraduate and graduate students with hands-on tools. In a highly cited 2022 study (17 citations), he advanced the field by combining adaptive neural networks with input-output linearization to ensure robust trajectory tracking for self-balancing robots, rigorously analyzing both external and internal dynamics. His 2025 work on PID control with neural network compensation further solidifies his impact, while his 2017 paper on model reference adaptive control (MRAC) for electrically driven robots tackles parameter uncertainties with elegant solutions. Aguilar-Avelar’s research is distinguished by its dual focus: pushing the boundaries of intelligent control theory while creating reproducible, educational methodologies that train the next generation of engineers.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3
- 4
- 5