Improved Prediction Model of the Friction Error of CNC Machine Tools Based on the Long Short Term Memory Method Article Swipe

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YOU? · · 2023 · Open Access · · DOI: https://doi.org/10.3390/machines11020243

Friction is one of important factors that cause contouring errors, and the friction error is difficult to predict because of its nonlinearity. In this paper, a prediction model of the friction error of a servo system is proposed based on the Long Short-Term Memory method (LSTM). Firstly, the transfer function is used to predict the position of the servo system, and then the prediction error of the transfer function is obtained. Secondly, the nonlinear friction error is extracted and predicted by a LSTM network. Finally, the accurate tracking error can be predicted by the proposed combined model. The experimental results show that the proposed model can improve the prediction accuracy of tracking errors dramatically.

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Nonlinear system Servomechanism Computer science Tracking error Control theory (sociology) Long short term memory Term (time) Position (finance) Mean squared prediction error Servo Transfer function Function (biology) Tracking (education) Approximation error Error detection and correction Position error Error function Algorithm Artificial intelligence Artificial neural network Control engineering Engineering Mathematics Control (management) Recurrent neural network Orientation (vector space) Quantum mechanics Economics Biology Geometry Physics Electrical engineering Evolutionary biology Psychology Pedagogy Finance

Metadata

Type: article
Language: en
Landing Page: https://doi.org/10.3390/machines11020243
PDF: https://www.mdpi.com/2075-1702/11/2/243/pdf?version=1675993897
OA Status: gold
Cited By: 3
References: 30
Related Works: 10
OpenAlex ID: https://openalex.org/W4319440461

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