F.J. Carrillo
Papers
5
Total Citations
61
H-Index
3
About
F.J. Carrillo is a leading researcher in the field of industrial robotics and electromechanical system identification, with a focus on developing rigorous methods for modeling complex, closed-loop dynamic systems. His major contributions center on improving the accuracy and reliability of parameter estimation for industrial robots and electrical drives. Carrillo’s most cited work, “An improved instrumental variable method for industrial robot model identification” (2018, 46 citations), introduces a refined approach that addresses the inherent challenges of identifying robot dynamics when systems must operate in closed-loop to maintain stability. He has systematically advanced the state of the art by comparing and validating methods such as the Inverse Dynamic Identification Model with Instrumental Variable (IDIM-IV) and the Direct and Inverse Dynamic Identification Models (DIDIM) method, as seen in his 2017 comparative study. His research is highly pragmatic, offering systematic statistical analyses that enable more reliable monitoring, control, and predictive maintenance of industrial robots and machine tools. With a career spanning foundational work on DC electrical drives (2004) to modern automated identification techniques (2018), Carrillo’s contributions are essential for engineers seeking to enhance the performance and availability of automated manufacturing systems.
Research Focus
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Top Papers
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