Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders

Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel l...

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Autores principales:	Jianfei Huang, Xinchun Cheng, Yuying Shen, Dewen Kong, Jixin Wang
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning wheel loaders throttle prediction state prediction automation Technology T
Acceso en línea:	https://doaj.org/article/dbea8f638b3d4011b4bb15b459b2497a
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id	oai:doaj.org-article:dbea8f638b3d4011b4bb15b459b2497a
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spelling	oai:doaj.org-article:dbea8f638b3d4011b4bb15b459b2497a2021-11-11T15:58:23ZDeep Learning-Based Prediction of Throttle Value and State for Wheel Loaders10.3390/en142172021996-1073https://doaj.org/article/dbea8f638b3d4011b4bb15b459b2497a2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/21/7202https://doaj.org/toc/1996-1073Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery.Jianfei HuangXinchun ChengYuying ShenDewen KongJixin WangMDPI AGarticledeep learningwheel loadersthrottle predictionstate predictionautomationTechnologyTENEnergies, Vol 14, Iss 7202, p 7202 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning wheel loaders throttle prediction state prediction automation Technology T
spellingShingle	deep learning wheel loaders throttle prediction state prediction automation Technology T Jianfei Huang Xinchun Cheng Yuying Shen Dewen Kong Jixin Wang Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
description	Accurate prediction of the throttle value and state for wheel loaders can help to achieve autonomous operation, thereby reducing the cost and accident rate. However, existing methods based on a physical model cannot accurately reflect the operator’s driving habits and the interaction between wheel loaders and the environment. In this paper, a deep-learning-based prediction model is developed to predict the throttle value and state for wheel loaders by learning from driving data. Multiple long–short-term memory (LSTM) networks are used to extract the temporal features of different stages during the operation of the wheel loader. Two backward-propagation neural networks (BPNNs), which use the temporal feature extracted by LSTM as the input, are designed to output the final prediction results of throttle value and state, respectively. The proposed prediction model is trained and tested using the data from two different conditions. The end-to-end LSTM prediction model and BPNNs are used as benchmark models. The results indicate that the proposed prediction model has good prediction accuracy and adaptability. Furthermore, the relationship between the prediction performance and signal sampling frequency is also studied. The proposed prediction method that combines driving data and deep learning can make the throttle action conform to the decisions of an experienced operator, providing technical support for the autonomous operation of construction machinery.
format	article
author	Jianfei Huang Xinchun Cheng Yuying Shen Dewen Kong Jixin Wang
author_facet	Jianfei Huang Xinchun Cheng Yuying Shen Dewen Kong Jixin Wang
author_sort	Jianfei Huang
title	Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
title_short	Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
title_full	Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
title_fullStr	Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
title_full_unstemmed	Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders
title_sort	deep learning-based prediction of throttle value and state for wheel loaders
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/dbea8f638b3d4011b4bb15b459b2497a
work_keys_str_mv	AT jianfeihuang deeplearningbasedpredictionofthrottlevalueandstateforwheelloaders AT xinchuncheng deeplearningbasedpredictionofthrottlevalueandstateforwheelloaders AT yuyingshen deeplearningbasedpredictionofthrottlevalueandstateforwheelloaders AT dewenkong deeplearningbasedpredictionofthrottlevalueandstateforwheelloaders AT jixinwang deeplearningbasedpredictionofthrottlevalueandstateforwheelloaders
_version_	1718432443715813376

Deep Learning-Based Prediction of Throttle Value and State for Wheel Loaders

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