Construction and control parameter matching method of hydraulic pile hammer surrogate model combining transfer algorithm and knowledge embedding

Due to its complex application environment, hydraulic pile hammers are difficult to model and accurately grasp their motion laws in automation control. Therefore, to address the high-precision prediction problem of the motion law of hydraulic pile hammer systems under complex working conditions, a knowledge embedding surrogate model based on a dual neural network is designed, which can be divided into two parts: feature extraction network and prediction network. On this basis, the model introduces physics knowledge embedding technology, adds inequality constraints based on physical laws to the loss function, and introduces transfer learning algorithms to maintain high accuracy of the model under limited data conditions. The results showed that the PSM (γ = 0.5) model designed in the study had a median of -0.001 and a variance of 0.038 under undisturbed conditions, demonstrating high prediction stability. Under perturbation conditions, the median of the PSM (γ = 1.5) model was -0.003, with a variance of 0.031, indicating the highest prediction accuracy. From this, the research model has good prediction accuracy under complex working conditions, which can lay a technical foundation for predicting the motion law of hydraulic pile hammers and automatic control.