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Squeeze Film Air Bearing for Controlling the Shaft Positions Based on a Radial Basis Function Neural Network by Zhang, Pengfei (author);Yang, Siyong (author);Li, Wenjun (author);Shi, Minghui (author);Feng, Kai (author) is a Engineering article available to read on EtoBox.
What is Squeeze Film Air Bearing for Controlling the Shaft Positions Based on a Radial Basis Function Neural Network about?
This study presents a squeeze film air bearing (SFAB) with flexure pivot-tilting pads that can be used to control the shaft positions. As the vibration characteristics and coupling relationship of three pads in the bearings, which determine the position control accuracy, are highly complicated, an intelligence algorithm based on a radial basis function neural network is proposed to forecast the desired pad amplitudes for the shaft reaching target positions. The database of the intelligence algorithm established on the basis of the air lubrication theory model is verified via an experimental test. Different target shaft positions that construct various trajectories are then used as the inputs of the intelligence algorithm. The desired pad amplitudes can be directly outputted from the intelligence algorithm and reused as the initial parameters of the air lubrication theory model to recalculate the shaft positions. The recalculated and target positions are compared and found to be in excellent agreement. This finding proves that the presented SFABs can precisely control the shaft to reach the target positions or move at the intended trajectories with the assistance of an intelligence
Who reads Squeeze Film Air Bearing for Controlling the Shaft Positions Based on a Radial Basis Function Neural Network?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Zhang, Pengfei (author);Yang, Siyong (author);Li, Wenjun (author);Shi, Minghui (author);Feng, Kai (author)
- Publisher
- Elsevier BV
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)