Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.10055490
S. Yadav, S. Arya, Nitesh Tiwari
{"title":"Multi-objective metaheuristic optimised PI gains of model reference adaptive controlled induction motor drive for electric vehicle","authors":"S. Yadav, S. Arya, Nitesh Tiwari","doi":"10.1504/ijvp.2023.10055490","DOIUrl":"https://doi.org/10.1504/ijvp.2023.10055490","url":null,"abstract":"","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66690699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.10055648
Guangqiang Wu, Bowen Ruan
{"title":"Torsional vibration analysis and optimisation of a hybrid vehicle powertrain","authors":"Guangqiang Wu, Bowen Ruan","doi":"10.1504/ijvp.2023.10055648","DOIUrl":"https://doi.org/10.1504/ijvp.2023.10055648","url":null,"abstract":"","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66690765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.10059665
Yaprak Yalcin, Ozgen Akalin, Volkan Bekir Yangin
{"title":"High-speed trajectory following of a heavy-duty vehicle via adaptive nonlinear model predictive controller","authors":"Yaprak Yalcin, Ozgen Akalin, Volkan Bekir Yangin","doi":"10.1504/ijvp.2023.10059665","DOIUrl":"https://doi.org/10.1504/ijvp.2023.10059665","url":null,"abstract":"","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136202479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.133852
Daoyu Shen, Shilei Zhou, Nong Zhang
Coming with the rising focus of the driving comfort request, more efforts are being delivered into the study of suspension system. Comparing with other traditional control methods, the machine learning control strategy has demonstrated its optimality in dealing with different class of roads. The work presented in this paper is to apply twin delayed deep deterministic policy gradients (TD3) in suspension control which enables suspension controller to go beyond searching for an optimal set of system parameters from traditional control method in dealing with different class of pavements. To achieve this, a suspension model has been established together with a reinforcement learning algorithm and an input signal of pavement. The performance of the twin delayed reinforcement agent is compared against deep deterministic policy gradients (DDPG) and deep Q-learning (DQN) algorithms under different types of pavement. The simulation result shows its superiority, robustness and learning efficiency over other reinforcement learning algorithms.
{"title":"Twin delayed deep deterministic reinforcement learning application in vehicle electrical suspension control","authors":"Daoyu Shen, Shilei Zhou, Nong Zhang","doi":"10.1504/ijvp.2023.133852","DOIUrl":"https://doi.org/10.1504/ijvp.2023.133852","url":null,"abstract":"Coming with the rising focus of the driving comfort request, more efforts are being delivered into the study of suspension system. Comparing with other traditional control methods, the machine learning control strategy has demonstrated its optimality in dealing with different class of roads. The work presented in this paper is to apply twin delayed deep deterministic policy gradients (TD3) in suspension control which enables suspension controller to go beyond searching for an optimal set of system parameters from traditional control method in dealing with different class of pavements. To achieve this, a suspension model has been established together with a reinforcement learning algorithm and an input signal of pavement. The performance of the twin delayed reinforcement agent is compared against deep deterministic policy gradients (DDPG) and deep Q-learning (DQN) algorithms under different types of pavement. The simulation result shows its superiority, robustness and learning efficiency over other reinforcement learning algorithms.","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136003761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.133855
Khashayar Moridpour, Mohammad Hasan Shojaeefard, Masoud Masih Tehrani, Rambod Yahyaei
{"title":"Active variable geometry suspension system development for a small off-road vehicle","authors":"Khashayar Moridpour, Mohammad Hasan Shojaeefard, Masoud Masih Tehrani, Rambod Yahyaei","doi":"10.1504/ijvp.2023.133855","DOIUrl":"https://doi.org/10.1504/ijvp.2023.133855","url":null,"abstract":"","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136004343","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.10055702
R. Yahyaei, Masoud Masih Tehrani, Khashayar Moridpour, M. Shojaeefard
{"title":"Active variable geometry suspension system development for a small off-road vehicle","authors":"R. Yahyaei, Masoud Masih Tehrani, Khashayar Moridpour, M. Shojaeefard","doi":"10.1504/ijvp.2023.10055702","DOIUrl":"https://doi.org/10.1504/ijvp.2023.10055702","url":null,"abstract":"","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66690839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.10053671
Xin Kong, Baohua Wang, W. Gao, Zhaowen Deng
{"title":"Lateral stability control of distributed electric drive articulated heavy vehicles","authors":"Xin Kong, Baohua Wang, W. Gao, Zhaowen Deng","doi":"10.1504/ijvp.2023.10053671","DOIUrl":"https://doi.org/10.1504/ijvp.2023.10053671","url":null,"abstract":"","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"66690986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-01DOI: 10.1504/ijvp.2023.133853
Jinli Xie, Xiaojun Huang, cheng Li
To improve driving safety of the human-machine co-driving (HMC) vehicles, an HMC system with a dynamic authority allocation model is extended in the base of the parallel shared steering control system. The proposed system consists of a compensation control loop, a driver model control loop, and an automatic controller control loop. These three control loops can be coupled using the dynamic authority allocation model. According to the status of the driver and the HMC system, the authority allocation model dynamically changes the authority level of each control loop and coordinates each control loop to complete the driving task. The effectiveness of the extended system is verified by simulation. The results show that the impacts of different driving characteristics and states, interferences, and controller faults on the system can be weakened, and the full load working time of the trajectory tracking controller is reduced.
{"title":"Driving authority allocation model for human-machine co-driving system considering fault tolerant control","authors":"Jinli Xie, Xiaojun Huang, cheng Li","doi":"10.1504/ijvp.2023.133853","DOIUrl":"https://doi.org/10.1504/ijvp.2023.133853","url":null,"abstract":"To improve driving safety of the human-machine co-driving (HMC) vehicles, an HMC system with a dynamic authority allocation model is extended in the base of the parallel shared steering control system. The proposed system consists of a compensation control loop, a driver model control loop, and an automatic controller control loop. These three control loops can be coupled using the dynamic authority allocation model. According to the status of the driver and the HMC system, the authority allocation model dynamically changes the authority level of each control loop and coordinates each control loop to complete the driving task. The effectiveness of the extended system is verified by simulation. The results show that the impacts of different driving characteristics and states, interferences, and controller faults on the system can be weakened, and the full load working time of the trajectory tracking controller is reduced.","PeriodicalId":52169,"journal":{"name":"International Journal of Vehicle Performance","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136003054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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