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LuGre-Net: a hybrid neural network for friction modeling of feed systems in machine tools

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Abstract

Friction has a substantial impact on the performance of machine feed systems, and establishing a high-precision friction model is the main premise of model-based friction compensation. To address the low accuracy and difficult parameter identification of the mathematical friction model, as well as the generalization issue of the neural network model, a novel friction model called LuGre-Net that combines the framework of the mathematical LuGre model with a neural network is proposed in this paper. Inspired by the formulas of the LuGre model, the topology of LuGre-Net is developed, with the ranges of LuGre's parameters incorporated into the LuGre-Net network as a priori knowledge. The experimental results demonstrate that the proposed LuGre-Net achieves great prediction accuracy, with a root mean square error (RMSE) and maximum absolute error (MAE) of 0.04 and 0.13 Nm on the test set, respectively. The RMSE of LuGre-Net is 63.9 and 17.8% lower than that of LuGre and the back propagation neural network (BPNN), respectively, while the MAE of LuGre-Net is 53.7 and 38.1% lower. Additionally, LuGre-Net outperforms BPNN in terms of generalization and sample dependence.

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Data Availability

The datasets generated during and/or analysed during the current study are not publicly available due to the datasets also forms part of an ongoing study, but are available from the corresponding author on reasonable request.

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Acknowledgements

This research is funded by Department of Science and Technology of Hubei Province (2021AAB001). We also acknowledge the comments of anonymous reviewers.

Funding

This work was supported by Department of Science and Technology of Hubei Province (2021AAB001). Author Jianzhong Yang has received research support from Department of Science and Technology of Hubei Province.

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Authors and Affiliations

  1. School of Mechanical Science & Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Dehai Huang, Jianzhong Yang, Guangda Xu & Jiakang Chen

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  1. Dehai Huang
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  2. Jianzhong Yang
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  3. Guangda Xu
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Contributions

All authors contributed to the study conception and design. Formal analysis is performed by Dehai Huang, Jianzhong Yang, Guangda Xu. Methods are mainly implemented by Dehai Huang, Guangda Xu. Algorithm programming and experimental verification are mainly implemented by Jiakang Chen, Dehai Huang. Funding acquisition and project administration were performed by Jianzhong Yang. The first draft of the manuscript was written by Dehai Huang and all authors commented on previous versions of the manuscript.

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Correspondence to Guangda Xu.

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Huang, D., Yang, J., Xu, G. et al. LuGre-Net: a hybrid neural network for friction modeling of feed systems in machine tools. Nonlinear Dyn (2024). https://doi.org/10.1007/s11071-024-09674-w

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