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About

Lili Qu is a prominent researcher specializing in robotics fault diagnosis, fault-tolerant control, and intelligent sensing systems, with a particular focus on applying advanced deep learning architectures to real-world robotic challenges. Her most influential work, "Sensor and Actuator Fault Diagnosis for Robot Joint Based on Deep CNN" (2021, 49 citations), pioneered the use of deep convolutional neural networks (DCNN) to diagnose complex fault types — including gain errors, offset errors, and malfunctions — in robot joints, establishing a robust data-driven paradigm for robotic health monitoring. Building on this foundation, Qu extended her methodology to deep residual neural networks (ResNet-based architectures), further enhancing diagnostic accuracy and depth of analysis. Her 2022 work on fractional-order sliding mode control addresses critical challenges in fault-tolerant systems, offering innovative solutions to the persistent jitter problem in conventional sliding mode controllers. Additionally, her research on machine vision-based fault detection demonstrates her ability to tackle real-world complexities such as poor lighting and high texture variability in industrial environments. With a growing citation record exceeding 90 citations across her recent publications, Qu's interdisciplinary contributions are shaping the future of intelligent, self-diagnosing robotic systems.