Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network
To predict the variation in the displacement position of hydraulic cylinder piston rods, a neural network model is proposed to enhance the displacement control accuracy of hydraulic cylinders. The innovation of this paper is that by calculating the compressibility-induced flow loss of hydraulic flui...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1949 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850088299594514432 |
|---|---|
| author | Wei Cai Yirui Zhang Jianxin Zhang Shunshun Guo Rui Guo |
| author_facet | Wei Cai Yirui Zhang Jianxin Zhang Shunshun Guo Rui Guo |
| author_sort | Wei Cai |
| collection | DOAJ |
| description | To predict the variation in the displacement position of hydraulic cylinder piston rods, a neural network model is proposed to enhance the displacement control accuracy of hydraulic cylinders. The innovation of this paper is that by calculating the compressibility-induced flow loss of hydraulic fluid, mathematical models for both the internal and external leakage of hydraulic cylinders are established, identifying seven primary factors influencing piston rod displacement. Because there are many influencing factors and complex parameters different from traditional backpropagation (BP) neural network used in previous studies, this paper innovatively proposes a three-layer BP neural network ensemble model for predicting input–output characteristic curves of hydraulic cylinders. In the process of model improvement, a nonlinear adaptive decreasing weight mechanism is introduced to improve the optimization accuracy of the algorithm, facilitating the search for optimal solutions. The most reasonable weight and bias parameters are determined via the iterative training and testing of each BP neural network layer. This model enables the real-time prediction of piston rod displacement output curves after a specified time interval based on external input parameters. The predicted time is utilized to compensate for the response delays caused by directional valve switching and hydraulic fluid buffering, thereby enabling proactive displacement prediction. Validation results demonstrate that the maximum predicted displacement error is reduced to 0.5491 µm, with the model’s maximum runtime being 27.82 ms. The maximum allowable time allocated for directional valve switching and fluid buffering in the hydraulic system is extended to 74.57 ms, achieving the objective of enhancing both displacement control accuracy and operational efficiency. |
| format | Article |
| id | doaj-art-c9c3aaab362b4c348f62ffd65e156318 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-c9c3aaab362b4c348f62ffd65e1563182025-08-20T02:43:03ZengMDPI AGSensors1424-82202025-03-01256194910.3390/s25061949Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural NetworkWei Cai0Yirui Zhang1Jianxin Zhang2Shunshun Guo3Rui Guo4State Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao 066004, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, ChinaSchool of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, ChinaState Key Laboratory of Crane Technology, Yanshan University, Qinhuangdao 066004, ChinaTo predict the variation in the displacement position of hydraulic cylinder piston rods, a neural network model is proposed to enhance the displacement control accuracy of hydraulic cylinders. The innovation of this paper is that by calculating the compressibility-induced flow loss of hydraulic fluid, mathematical models for both the internal and external leakage of hydraulic cylinders are established, identifying seven primary factors influencing piston rod displacement. Because there are many influencing factors and complex parameters different from traditional backpropagation (BP) neural network used in previous studies, this paper innovatively proposes a three-layer BP neural network ensemble model for predicting input–output characteristic curves of hydraulic cylinders. In the process of model improvement, a nonlinear adaptive decreasing weight mechanism is introduced to improve the optimization accuracy of the algorithm, facilitating the search for optimal solutions. The most reasonable weight and bias parameters are determined via the iterative training and testing of each BP neural network layer. This model enables the real-time prediction of piston rod displacement output curves after a specified time interval based on external input parameters. The predicted time is utilized to compensate for the response delays caused by directional valve switching and hydraulic fluid buffering, thereby enabling proactive displacement prediction. Validation results demonstrate that the maximum predicted displacement error is reduced to 0.5491 µm, with the model’s maximum runtime being 27.82 ms. The maximum allowable time allocated for directional valve switching and fluid buffering in the hydraulic system is extended to 74.57 ms, achieving the objective of enhancing both displacement control accuracy and operational efficiency.https://www.mdpi.com/1424-8220/25/6/1949hydraulic cylinderdisplacement controlprediction modelBP neural network |
| spellingShingle | Wei Cai Yirui Zhang Jianxin Zhang Shunshun Guo Rui Guo Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network Sensors hydraulic cylinder displacement control prediction model BP neural network |
| title | Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network |
| title_full | Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network |
| title_fullStr | Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network |
| title_full_unstemmed | Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network |
| title_short | Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network |
| title_sort | prediction of input output characteristic curves of hydraulic cylinders based on three layer bp neural network |
| topic | hydraulic cylinder displacement control prediction model BP neural network |
| url | https://www.mdpi.com/1424-8220/25/6/1949 |
| work_keys_str_mv | AT weicai predictionofinputoutputcharacteristiccurvesofhydrauliccylindersbasedonthreelayerbpneuralnetwork AT yiruizhang predictionofinputoutputcharacteristiccurvesofhydrauliccylindersbasedonthreelayerbpneuralnetwork AT jianxinzhang predictionofinputoutputcharacteristiccurvesofhydrauliccylindersbasedonthreelayerbpneuralnetwork AT shunshunguo predictionofinputoutputcharacteristiccurvesofhydrauliccylindersbasedonthreelayerbpneuralnetwork AT ruiguo predictionofinputoutputcharacteristiccurvesofhydrauliccylindersbasedonthreelayerbpneuralnetwork |