Papers
6
Total Citations
20
H-Index
3
About
Josué Gómez is a researcher at the forefront of data-driven control theory, specializing in the development of intelligent, model-free controllers for complex robotic systems. His work bridges the gap between advanced computational intelligence and practical robotics, focusing on adaptive control, sliding mode techniques, and neuro-fuzzy networks. Gómez’s major contributions lie in creating robust control algorithms that operate without requiring precise mathematical models of the system, instead leveraging real-time data and feedback. His most cited work, "Data-driven identification and control based on optic tracking feedback for robotic systems" (5 citations), exemplifies this approach by integrating motion capture feedback for precise robot manipulation. He has also pioneered adaptive controllers using Double Fuzzy Rule Emulated Networks (FREN) and sliding mode methods for nonlinear discrete-time plants, as well as novel pseudo Jacobian matrix algorithms for task space control. Gómez’s research extends to soft robotics, where he has developed data-driven adaptive force control for ultrasonic atomization-based actuators. With a growing portfolio of publications from 2018 to 2022, his work is increasingly recognized for enabling autonomous, flexible, and model-free control in real-world robotic applications, making him a key figure in the evolution of intelligent robotic systems.
Research Focus
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