Data-Driven Driving State Control for Unmanned Agricultural Logistics Vehicle
Logistic activities widely exist in the agricultural production process. With the gradual application of unmanned technology in agricultural production, the technology related to intelligent logistics has become a research hotspot. Because of the complicated driving road conditions of agricultural l...
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| Veröffentlicht in: | IEEE access 2020, Vol.8, p.65530-65543 |
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| creator | Zhou, Xuesheng Zhou, Jun |
| description | Logistic activities widely exist in the agricultural production process. With the gradual application of unmanned technology in agricultural production, the technology related to intelligent logistics has become a research hotspot. Because of the complicated driving road conditions of agricultural logistics vehicles, vehicle stability control has become a key problem for realizing intelligent driving. In this paper, the control of unmanned agricultural logistics vehicles in complex farmland environment is studied to provide data support for the intelligent driving of agricultural vehicles. In the current paper, based on the characteristics of the four-wheel drive independent control moment response of distributed electric agricultural vehicle, a coordinated stability control method based on the improved adaptive model predictive control (MPC) is proposed. In the paper, an unmanned agricultural logistics vehicle platform is developed. The front and rear radars mounted on the platform is used to scan the targets and obstacles in the operation site, and a relevant target and position coordinate database is established to provide data support for the intelligent driving of agricultural logistics vehicles. In the paper, the information base established by the front and rear radar scanning can control the driving attitude and path of the agricultural logistics transportation platform and realize the intelligent agricultural logistics. |
| doi_str_mv | 10.1109/ACCESS.2020.2983424 |
| format | Article |
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With the gradual application of unmanned technology in agricultural production, the technology related to intelligent logistics has become a research hotspot. Because of the complicated driving road conditions of agricultural logistics vehicles, vehicle stability control has become a key problem for realizing intelligent driving. In this paper, the control of unmanned agricultural logistics vehicles in complex farmland environment is studied to provide data support for the intelligent driving of agricultural vehicles. In the current paper, based on the characteristics of the four-wheel drive independent control moment response of distributed electric agricultural vehicle, a coordinated stability control method based on the improved adaptive model predictive control (MPC) is proposed. In the paper, an unmanned agricultural logistics vehicle platform is developed. The front and rear radars mounted on the platform is used to scan the targets and obstacles in the operation site, and a relevant target and position coordinate database is established to provide data support for the intelligent driving of agricultural logistics vehicles. In the paper, the information base established by the front and rear radar scanning can control the driving attitude and path of the agricultural logistics transportation platform and realize the intelligent agricultural logistics.</description><identifier>ISSN: 2169-3536</identifier><identifier>EISSN: 2169-3536</identifier><identifier>DOI: 10.1109/ACCESS.2020.2983424</identifier><identifier>CODEN: IAECCG</identifier><language>eng</language><publisher>Piscataway: IEEE</publisher><subject>Adaptive control ; agricultural big data structure ; Agricultural land ; Agricultural production ; Agricultural vehicles ; Control methods ; Control stability ; Driving conditions ; Four wheel drive ; Logistic activities ; Logistics ; Mathematical model ; model predictive control ; Optimization ; Predictive control ; Predictive models ; Radar scanning ; Stability analysis ; unmanned driving technology ; Vehicles ; Wheels</subject><ispartof>IEEE access, 2020, Vol.8, p.65530-65543</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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With the gradual application of unmanned technology in agricultural production, the technology related to intelligent logistics has become a research hotspot. Because of the complicated driving road conditions of agricultural logistics vehicles, vehicle stability control has become a key problem for realizing intelligent driving. In this paper, the control of unmanned agricultural logistics vehicles in complex farmland environment is studied to provide data support for the intelligent driving of agricultural vehicles. In the current paper, based on the characteristics of the four-wheel drive independent control moment response of distributed electric agricultural vehicle, a coordinated stability control method based on the improved adaptive model predictive control (MPC) is proposed. In the paper, an unmanned agricultural logistics vehicle platform is developed. The front and rear radars mounted on the platform is used to scan the targets and obstacles in the operation site, and a relevant target and position coordinate database is established to provide data support for the intelligent driving of agricultural logistics vehicles. 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The front and rear radars mounted on the platform is used to scan the targets and obstacles in the operation site, and a relevant target and position coordinate database is established to provide data support for the intelligent driving of agricultural logistics vehicles. In the paper, the information base established by the front and rear radar scanning can control the driving attitude and path of the agricultural logistics transportation platform and realize the intelligent agricultural logistics.</abstract><cop>Piscataway</cop><pub>IEEE</pub><doi>10.1109/ACCESS.2020.2983424</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-1651-2468</orcidid><orcidid>https://orcid.org/0000-0001-6516-8232</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adaptive control agricultural big data structure Agricultural land Agricultural production Agricultural vehicles Control methods Control stability Driving conditions Four wheel drive Logistic activities Logistics Mathematical model model predictive control Optimization Predictive control Predictive models Radar scanning Stability analysis unmanned driving technology Vehicles Wheels |
| title | Data-Driven Driving State Control for Unmanned Agricultural Logistics Vehicle |
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