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About

Xingguo Lu is a leading researcher in the field of intelligent control systems, with a primary focus on the design, optimization, and application of advanced fuzzy logic controllers for robotic systems. His major contributions center on the development of interval type-2 fuzzy neural network and PID-type fuzzy logic controllers, specifically tailored for the high-precision trajectory control of Delta parallel robots. Lu’s work is distinguished by its systematic approach to controller optimization, notably employing particle swarm optimization (PSO) to tune membership functions and controller gains, thereby significantly enhancing tracking accuracy and robustness. His most-cited papers, including a 2017 study on self-learning interval type-2 fuzzy neural network controllers (40 citations) and a 2016 work on optimal PID-type interval type-2 fuzzy logic controller design (29 citations), have collectively garnered over 129 citations, underscoring their influence in robotics and control engineering. By addressing the critical challenge of parameter tuning in nonlinear, uncertain environments, Lu has provided practical methodologies that bridge theoretical fuzzy control with real-world robotic applications, making his research essential reading for students and engineers advancing intelligent automation.