Jinkun Liu
Papers
14
Total Citations
417
H-Index
9
About
Jinkun Liu is a leading authority in advanced control systems, specializing in neural network control, sliding mode control, and the dynamics of flexible robotic manipulators. His most influential work, *Radial Basis Function (RBF) Neural Network Control for Mechanical Systems* (144 citations), has become a cornerstone for researchers tackling nonlinear mechanical systems, providing both theoretical frameworks and practical MATLAB simulation tools. Liu’s contributions extend to the widely-used *Sliding Mode Control Using MATLAB* (81 citations), which demystifies robust controller design for continuous and adaptive systems. He has pioneered control strategies for unconventional platforms, such as the spherical robot BHQ-1 (54 citations), addressing nonholonomic path following, and has advanced the field of flexible robotics with adaptive boundary controllers for arm-string systems (29 citations) and space robots with multi-link flexible manipulators (24 citations). His recent work on consensus control for multiple flexible Timoshenko manipulators (2024) and reinforcement learning-based vibration suppression (2017) demonstrates a continued commitment to solving complex, real-world challenges. With over 400 total citations, Liu’s research is essential reading for engineers and students working on intelligent, adaptive, and flexible robotic systems.
Research Focus
Key Achievements
Top Papers
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- 2Sliding Mode Control Using MATLAB81 citations · 2017
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- 5Adaptive RBF neural network control of robot with actuator nonlinearities29 citations · 2010
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