دورية أكاديمية
Position Soft-Sensing of Direct-Driven Hydraulic System Based on Back Propagation Neural Network
| العنوان: | Position Soft-Sensing of Direct-Driven Hydraulic System Based on Back Propagation Neural Network |
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| المؤلفون: | Shuzhong Zhang, Tianyi Chen, Tatiana Minav, Xuepeng Cao, Angeng Wu, Yi Liu, Xuefeng Zhang |
| المصدر: | Actuators, Vol 10, Iss 12, p 322 (2021) |
| بيانات النشر: | MDPI AG, 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Materials of engineering and construction. Mechanics of materials LCC:Production of electric energy or power. Powerplants. Central stations |
| مصطلحات موضوعية: | direct-driven hydraulics, position soft-sensing, BP neural network, accumulative error correction, virtual sensors, Materials of engineering and construction. Mechanics of materials, TA401-492, Production of electric energy or power. Powerplants. Central stations, TK1001-1841 |
| الوصف: | Automated operations are widely used in harsh environments, in which position information is essential. Although sensors can be equipped to obtain high-accuracy position information, they are quite expensive and unsuitable for harsh environment applications. Therefore, a position soft-sensing model based on a back propagation (BP) neural network is proposed for direct-driven hydraulics (DDH) to protect against harsh environmental conditions. The proposed model obtains a position by integrating velocity computed from the BP neural network, which trains the nonlinear relationship between multi-input (speed of the electric motor and pressures in two chambers of the cylinder) and single-output (the cylinder’s velocity). First, the model of a standalone crane with DDH was established and verified by experiment. Second, the data from batch simulation with the verified model was used for training and testing the BP neural network in the soft-sensing model. Finally, position estimation with a typical cycle was performed using the created position soft-sensing model. Compared with the experimental data, the maximum soft-sensing position error was about 7 mm, and the error rate was within ±2.5%. Furthermore, position estimations were carried out with the proposed soft-sensing model under differing working conditions and the errors were within 4 mm, but the periodically cumulative error was observed. Hence, a reference point is proposed to minimize the accumulative error, for example, a point at the middle of the cylinder. Therefore, the work can be applied to acquire position information to facilitate automated operation of machines equipped with DDH. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2076-0825 |
| Relation: | https://www.mdpi.com/2076-0825/10/12/322; https://doaj.org/toc/2076-0825 |
| DOI: | 10.3390/act10120322 |
| URL الوصول: | https://doaj.org/article/72120b2183ed4cc883b8caf5400f9d86 |
| رقم الأكسشن: | edsdoj.72120b2183ed4cc883b8caf5400f9d86 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 20760825 |
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| DOI: | 10.3390/act10120322 |