Qijie Chen
Papers
1
Total Citations
1
H-Index
1
About
Qijie Chen is a researcher specializing in intelligent control systems and robotics, with a particular focus on trajectory tracking and adaptive control for robotic manipulators. Their major contribution lies in the integration of wavelet neural networks (WNN) with sliding mode control (SMC) to address the challenge of precise trajectory tracking under periodic interference—a common issue in space robotics and industrial automation. In their most cited work, "Trajectory Tracking of Two-Joint Space Robot using Wavelet Neural Networks and Sliding Mode Control" (2022), Chen proposed a novel control algorithm that leverages the adaptive learning capabilities of WNN to enhance the robustness of SMC, effectively mitigating disturbances and improving tracking accuracy. This work, while still early in its citation impact, demonstrates Chen’s commitment to advancing control theory for complex, real-world applications. Their research is particularly relevant for students and researchers interested in nonlinear control, neural network-based systems, and the intersection of machine learning with mechanical engineering. Chen’s work lays a foundation for future developments in autonomous robotic systems, especially in environments where precision and disturbance rejection are critical.
Research Focus
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Top Papers
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