Continuous Damping Control (CDC) damper is a kind of active damper with adjustable damping coefficients, which has a broad application prospect for vibration attenuation in robots and manufacturing systems. To develop a digital twin of CDC damper for monitoring its working conditions, it is necessary to develop a physical model describing the characteristics of CDC damper. However, the existing physical models of CDC damper have some disadvantages such as expensive computing costs and low accuracy, which make it difficult to apply them in the digital twin. In order to solve these issues, this paper proposes a hybrid model which can accurately describe the physical characteristics of CDC damper by combining a lumped parameter model and a neural network model. First, the physical characteristics of a CDC damper are measured on a test rig under harmonic excitations and random excitations. Then the modeling and parameter identification methods for hybrid models are proposed to obtain model parameters. Finally, the output force of the CDC damper under random excitation is calculated using the hybrid model, and the accuracy of the hybrid model is validated after comparing the calculated results with the measured results. The proposed hybrid model has high accuracy and low computational cost, and is applicable to developing digital twins of CDC dampers which require high calculation accuracy and efficiency.
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