{"title":"电动传动系统的自适应神经网络模型预测抗急跳控制","authors":"Gianluca Frison, Fabio Alberti, Luca Ciravegna, Luca Dimauro, Aldo Sorniotti","doi":"10.1016/j.mechmachtheory.2025.106082","DOIUrl":null,"url":null,"abstract":"<div><div>The study introduces a proof-of-concept self-adaptive neural network model predictive control (SA-NNMPC) system, which uses a neural network as main component of the prediction model, for the anti-jerk control of electric vehicles. Through the adaptation mechanism of the network and cost function weights during vehicle operation, which is activated when the plant behaves significantly differently from its digital twin, the SA-NNMPC architecture adjusts to the progressive vehicle aging, or to the replacement of hardware parts, which is expected to be an important feature of next-generation vehicles. Validation tests and simulations show that the neural network accurately replicates the drivetrain dynamics of the considered electric vehicle, and, for nominal conditions, already leads to a performance improvement of the NNMPC implementation – which can run in real-time on a rapid control prototyping unit – with respect to a benchmarking nonlinear model predictive anti-jerk controller. Moreover, the preliminary simulation results confirm the potential of the proposed architecture in terms of: i) adaptability to operating conditions not covered in the original training, and variations of vehicle parameters; and ii) auto-tuning of the algorithm when applied to different vehicles.</div></div>","PeriodicalId":49845,"journal":{"name":"Mechanism and Machine Theory","volume":"214 ","pages":"Article 106082"},"PeriodicalIF":4.5000,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Self-adaptive neural network model predictive anti-jerk control of electric powertrains\",\"authors\":\"Gianluca Frison, Fabio Alberti, Luca Ciravegna, Luca Dimauro, Aldo Sorniotti\",\"doi\":\"10.1016/j.mechmachtheory.2025.106082\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The study introduces a proof-of-concept self-adaptive neural network model predictive control (SA-NNMPC) system, which uses a neural network as main component of the prediction model, for the anti-jerk control of electric vehicles. Through the adaptation mechanism of the network and cost function weights during vehicle operation, which is activated when the plant behaves significantly differently from its digital twin, the SA-NNMPC architecture adjusts to the progressive vehicle aging, or to the replacement of hardware parts, which is expected to be an important feature of next-generation vehicles. Validation tests and simulations show that the neural network accurately replicates the drivetrain dynamics of the considered electric vehicle, and, for nominal conditions, already leads to a performance improvement of the NNMPC implementation – which can run in real-time on a rapid control prototyping unit – with respect to a benchmarking nonlinear model predictive anti-jerk controller. Moreover, the preliminary simulation results confirm the potential of the proposed architecture in terms of: i) adaptability to operating conditions not covered in the original training, and variations of vehicle parameters; and ii) auto-tuning of the algorithm when applied to different vehicles.</div></div>\",\"PeriodicalId\":49845,\"journal\":{\"name\":\"Mechanism and Machine Theory\",\"volume\":\"214 \",\"pages\":\"Article 106082\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2025-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechanism and Machine Theory\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0094114X25001715\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechanism and Machine Theory","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0094114X25001715","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Self-adaptive neural network model predictive anti-jerk control of electric powertrains
The study introduces a proof-of-concept self-adaptive neural network model predictive control (SA-NNMPC) system, which uses a neural network as main component of the prediction model, for the anti-jerk control of electric vehicles. Through the adaptation mechanism of the network and cost function weights during vehicle operation, which is activated when the plant behaves significantly differently from its digital twin, the SA-NNMPC architecture adjusts to the progressive vehicle aging, or to the replacement of hardware parts, which is expected to be an important feature of next-generation vehicles. Validation tests and simulations show that the neural network accurately replicates the drivetrain dynamics of the considered electric vehicle, and, for nominal conditions, already leads to a performance improvement of the NNMPC implementation – which can run in real-time on a rapid control prototyping unit – with respect to a benchmarking nonlinear model predictive anti-jerk controller. Moreover, the preliminary simulation results confirm the potential of the proposed architecture in terms of: i) adaptability to operating conditions not covered in the original training, and variations of vehicle parameters; and ii) auto-tuning of the algorithm when applied to different vehicles.
期刊介绍:
Mechanism and Machine Theory provides a medium of communication between engineers and scientists engaged in research and development within the fields of knowledge embraced by IFToMM, the International Federation for the Promotion of Mechanism and Machine Science, therefore affiliated with IFToMM as its official research journal.
The main topics are:
Design Theory and Methodology;
Haptics and Human-Machine-Interfaces;
Robotics, Mechatronics and Micro-Machines;
Mechanisms, Mechanical Transmissions and Machines;
Kinematics, Dynamics, and Control of Mechanical Systems;
Applications to Bioengineering and Molecular Chemistry