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Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered

文献类型：期刊论文

作者Jin, Long1; Liu, Longqi1; Wang, Xingxia2,3; Shang, Mingsheng4; Wang, Fei-Yue5,6
刊名IEEE TRANSACTIONS ON INTELLIGENT VEHICLES
出版日期2024-03-01
卷号9期号:3页码:4493-4503
关键词Mathematical models Trajectory tracking Task analysis Predictive models Intelligent vehicles Computational modeling Trajectory Artificial systems computational experiments model predictive control (MPC) controller parallel execution (ACP) physical-informed neural network (PINN) trajectory tracking tasks
ISSN号2379-8858
DOI10.1109/TIV.2024.3358229
通讯作者Wang, Fei-Yue(feiyue.wang@ia.ac.cn)
英文摘要The trajectory tracking plays a vital role in unmanned driving technology. Although traditional control schemes may yield satisfactory outcomes in dealing with simple linear tasks, they may fall short when handling dynamic systems with time-varying characteristics or lack of ability to complete a given task with the disturbance of noise. Therefore, a predictive control scheme under the framework of artificial systems, computational experiments, and parallel execution (ACP) is proposed. Within the ACP framework, the scheme integrates a model predictive control (MPC) controller and a physical-informed neural network (PINN) model to tackle intricate trajectory tracking tasks effectively with noise considered. Moreover, soft constraints that can enhance model robustness and improve solution efficiency are considered in the scheme. Then, theoretical analyses on the PINN model are provided with rigorous mathematical proofs. Finally, experiments and comparisons with existing works are conducted to illustrate the effectiveness and superiority of the constructed PINN model for MPC-based trajectory tracking of vehicles.
WOS关键词MODEL-PREDICTIVE CONTROL ; SYSTEMS
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Engineering ; Transportation
语种英语
WOS记录号WOS:001214544700028
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/58363]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Wang, Fei-Yue
作者单位1.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Chongqing Key Lab Big Data & Intelligent Comp, Chongqing 400714, Peoples R China
5.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
6.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
推荐引用方式
GB/T 7714		 Jin, Long,Liu, Longqi,Wang, Xingxia,et al. Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2024,9(3):4493-4503.
APA Jin, Long,Liu, Longqi,Wang, Xingxia,Shang, Mingsheng,&Wang, Fei-Yue.(2024).Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,9(3),4493-4503.
MLA Jin, Long,et al."Physical-Informed Neural Network for MPC-Based Trajectory Tracking of Vehicles With Noise Considered".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 9.3(2024):4493-4503.

入库方式： OAI收割

来源：自动化研究所

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