Guanbin Gao
Papers
29
Total Citations
651
H-Index
12
About
Guanbin Gao is a leading researcher in robotic systems, with a primary focus on motion control, kinematic calibration, and parameter identification for industrial manipulators. His most significant contribution is the development of the Unknown System Dynamics Estimator (USDE), a simple yet efficient method for handling unknown dynamics and external disturbances in robotic motion control, which has garnered 153 citations. Gao has also pioneered hybrid identification methods, such as combining BP neural networks with particle swarm optimization (BPNN-PSO), to enhance the kinematic accuracy of industrial robots and articulated arm coordinate measuring machines (AACMMs). His work on adaptive neural network control for robotic manipulators, with guaranteed finite-time convergence, has been cited 92 times, demonstrating its impact on improving robot precision and reliability. With over 500 total citations across his top papers, Gao’s research addresses critical challenges in robotics, including error compensation, structural parameter identification, and 3D measurement systems. His notable achievements include advancing calibration techniques that avoid direct end-effector coordinate measurements, making his methods practical for real-world industrial applications. Gao’s work is essential reading for students and researchers interested in the intersection of control theory, neural networks, and robotic kinematics.
Research Focus
Key Achievements
Top Papers
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- 2Structural parameter identification for 6 DOF industrial robots133 citations · 2017
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- 8Kinematic analysis and simulation of a 3-DOF robotic manipulator20 citations · 2017
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