Luis Arturo Soriano
Chapingo Autonomous University, Instituto Politécnico Nacional, Tecnológico de Monterrey
Papers
8
Total Citations
256
H-Index
6
About
Luis Arturo Soriano is a leading researcher in robotics and control systems, with a focus on enhancing the performance and energy efficiency of robotic manipulators. His work centers on advanced control strategies, including Proportional-Integral-Derivative (PID) compensation, sliding mode control, and neural networks, to address challenges like stability, degradation, and energy consumption in industrial robots. Soriano’s most cited paper, "PD Control Compensation Based on a Cascade Neural Network Applied to a Robot Manipulator" (2020), with 98 citations, introduces a novel approach to mitigate integral gain degradation in PID controllers. Another key contribution, "Optimization of Sliding Mode Control to Save Energy in a SCARA Robot" (2021), with 58 citations, optimizes robust control to reduce energy use while maintaining precision. His "Modified Linear Technique for the Controllability and Observability of Robotic Arms" (2022), with 56 citations, offers a transformative method for analyzing nonlinear robotic systems. Soriano also explores power electronics longevity using fuzzy logic and particle swarm optimization, and virtual laboratory design for engineering education. With over 250 total citations, his research bridges theoretical control theory and practical robotics, making significant impacts on automation and sustainable manufacturing.
Research Focus
Key Achievements
Top Papers
- 1
- 2Optimization of Sliding Mode Control to Save Energy in a SCARA Robot58 citations · 2021
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- 6A Novel Design of Virtual Laboratory9 citations · 2019
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