Papers
5
Total Citations
35
H-Index
4
About
Juan F. Guerra is a robotics researcher whose work sits at the intersection of intelligent control systems, cable-driven parallel robots (CDPRs), and neural network-based identification. His research is driven by a practical commitment to solving real-world problems, as evidenced by his most-cited work—a 2021 study proposing a decoupled fuzzy-PID controller for planar CDPRs (14 citations), which offers a simpler, more practical approach to managing the nonlinearities inherent in cable and pulley systems. Guerra has also made significant contributions to the application of swarm intelligence metaheuristics—comparing algorithms like ALO, BA, GWO, and MFO in a 2023 study (7 citations)—to enhance neural training for robotic manipulator control. A notable achievement is his application of a planar CDPR for transporting supplies to patients with contagious diseases, directly addressing challenges posed by the COVID-19 pandemic (6 citations). Further, his work on UKF-based neural training (5 citations) and decentralized neural block control (3 citations) demonstrates a sustained focus on improving nonlinear system identification and trajectory tracking. Through these contributions, Guerra is advancing both the theory and practice of intelligent, adaptive robotic systems for healthcare and industrial applications.
Research Focus
Key Achievements
Top Papers
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