Papers
4
Total Citations
56
H-Index
4
About
Dr. Pu Zhao is a robotics researcher whose work focuses on the critical challenges of friction modeling, state estimation, and vibration control in robotic manipulators. His research addresses the practical limitations of industrial robots, particularly those caused by joint flexibility from harmonic drives and bearing deformation. Dr. Zhao’s most influential contribution is his development of a genetically optimized BP neural network for modeling static friction in robot joints, which has garnered 38 citations and provides a data-driven solution to a notoriously difficult nonlinear problem. He has also advanced the field through parameter identification techniques using optimal exciting trajectories to improve friction model efficiency, and by formulating a novel nonlinear tracking differentiator for state estimation in noisy environments. Additionally, his work on active vibration control using accelerometers offers practical solutions for flexible-joint manipulators, a common issue in industrial settings where only motor-side encoders are available. Dr. Zhao’s research bridges the gap between theoretical modeling and real-world robotic performance, making his contributions valuable for engineers and researchers seeking to enhance the precision and reliability of robotic systems.
Research Focus
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