Shovel-loading cooperative control of loader under typical working conditions

被引：5
作者：
Cao, Bing-Wei [1 ,2 ]
Liu, Chang-Yi [1 ,3 ]
Chen, Wei [1 ]
Tan, Peng [1 ]
Yang, Jian-Wen [1 ]
机构：
[1] Jilin Univ, Coll Mech & Aerosp Engn, Changchun 130025, Peoples R China
[2] Jilin Univ, Weihai Inst Bion, Weihai 264200, Peoples R China
[3] Jilin Univ, Key Lab Bion Engn, Minist Educ, Changchun 130025, Peoples R China
来源：
ISA TRANSACTIONS | 2023年 / 142卷
关键词：
Shovel -loading cooperative; Driving intention; Load characteristic; Power output characteristics; Operating stage identification; ALGORITHM; SVM;
D O I：
10.1016/j.isatra.2023.07.010
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The difference in power demand and the driver's operation in various operation stages make the loader have the problem of low energy utilization. Changeable operating objects and drastically changing loads have exacerbated the difficulty of energy-saving research in different operating stages of loaders. Therefore, based on identifying the operation stage and analyzing the load characteristics under typical working conditions of the loader, this paper proposes a shovel-loading cooperative control strategy. Specifically, the Drag Reduction Insertion (DRI) in the shoveling stage is realized based on learning the driving intention. Based on different operation stages, the load characteristics of different materials for shovel-loading are deeply analyzed, and the prediction research of the power output characteristics of the power unit is carried out. The shovel-loading cooperative control solves the problem of the poor economy caused by different power requirements in different operation stages and significantly reduces the impact of the driver's operating experience. (c) 2023 ISA. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：702 / 715
页数：14
相关论文
共 52 条
  • [1] DeepImpact: a deep learning model for whole body vibration control using impact force monitoring
    Ali, Danish
    Frimpong, Samuel
    [J]. NEURAL COMPUTING & APPLICATIONS, 2021, 33 (08) : 3521 - 3544
  • [2] Tyre modelling for an autonomous articulated wheel loader conducting a V-shape loading cycle simulation
    Alshaer, Bassam J.
    Al-Ghasem, Adnan M.
    Al-Shyyab, Ahmad S.
    Alhanouti, Muhammed
    [J]. INTERNATIONAL JOURNAL OF HEAVY VEHICLE SYSTEMS, 2017, 24 (03) : 199 - 214
  • [3] Intelligentization of wheel loader shoveling system based on multi-source data acquisition
    Cao, Bingwei
    Liu, Xinhui
    Chen, Wei
    Li, Haomin
    Wang, Xianqing
    [J]. AUTOMATION IN CONSTRUCTION, 2023, 147
  • [4] Design and modeling of hardware-in-loop test bench for hydraulic excavator based on dynamic load emulation
    Chen, Jinshi
    Huo, Dongyang
    Liu, Changyi
    Zhang, Han
    Wang, Yuqi
    [J]. AUTOMATION IN CONSTRUCTION, 2022, 137
  • [5] Chen TB, 2019, J ANAL ATOM SPECTROM, V34, P884, DOI [10.1039/c8ja00392k, 10.1039/C8JA00392K]
  • [6] Research on the Trajectory and Operational Performance of Wheel Loader Automatic Shoveling
    Chen, Yanhui
    Jiang, Heng
    Shi, Gang
    Zheng, Te
    [J]. APPLIED SCIENCES-BASEL, 2022, 12 (24):
  • [7] Simulation and optimization of the tracked chassis performance of electric shovel based on DEM-MBD
    Chen, Zeren
    Xue, Duomei
    Wang, Guoqiang
    Cui, Da
    Fang, Yi
    Wang, Shuai
    [J]. POWDER TECHNOLOGY, 2021, 390 : 428 - 441
  • [8] Landslide Susceptibility Mapping along the Anninghe Fault Zone in China using SVM and ACO-PSO-SVM Models
    Chen, Zhuo
    Zhou, Hongfu
    Ye, Fei
    Liu, Bin
    Fu, Wenxi
    [J]. LITHOSPHERE, 2022, 2022 (01) : 2 - 16
  • [9] Discrete element parameter calibration and the modelling of dragline bucket filling
    Coetzee, C. J.
    Els, D. N. J.
    Dymond, G. F.
    [J]. JOURNAL OF TERRAMECHANICS, 2010, 47 (01) : 33 - 44
  • [10] Key challenges in automation of earth-moving machines
    Dadhich, S.
    Bodin, U.
    Andersson, U.
    [J]. AUTOMATION IN CONSTRUCTION, 2016, 68 : 212 - 222
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