M. Carratù, A. Pietrosanto, P. Sommella, V. Paciello
{"title":"通过加速度积分测量悬架速度","authors":"M. Carratù, A. Pietrosanto, P. Sommella, V. Paciello","doi":"10.1109/INDIN.2018.8472039","DOIUrl":null,"url":null,"abstract":"Semi-active suspension control is able to regulate the damping forces by measuring the relative velocity of the wheels respect to the vehicle body. The adoption of linear potentiometers (the most used sensors in racing for linearity and simplicity) reveals to be expensive for mass market and unreliable in the long run. The paper deals with the validation of a soft sensor for the online estimation of the suspension velocity from acceleration signals. Main features of the soft sensor implementation according to different approaches (based on time-domain integration and Artificial Neural Networks respectively) are discussed with reference to the typical requirements for the real-time prediction of the suspension velocity.","PeriodicalId":6467,"journal":{"name":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","volume":"29 1","pages":"933-938"},"PeriodicalIF":0.0000,"publicationDate":"2018-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Measuring suspension velocity from acceleration integration\",\"authors\":\"M. Carratù, A. Pietrosanto, P. Sommella, V. Paciello\",\"doi\":\"10.1109/INDIN.2018.8472039\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Semi-active suspension control is able to regulate the damping forces by measuring the relative velocity of the wheels respect to the vehicle body. The adoption of linear potentiometers (the most used sensors in racing for linearity and simplicity) reveals to be expensive for mass market and unreliable in the long run. The paper deals with the validation of a soft sensor for the online estimation of the suspension velocity from acceleration signals. Main features of the soft sensor implementation according to different approaches (based on time-domain integration and Artificial Neural Networks respectively) are discussed with reference to the typical requirements for the real-time prediction of the suspension velocity.\",\"PeriodicalId\":6467,\"journal\":{\"name\":\"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)\",\"volume\":\"29 1\",\"pages\":\"933-938\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/INDIN.2018.8472039\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE 16th International Conference on Industrial Informatics (INDIN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/INDIN.2018.8472039","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Measuring suspension velocity from acceleration integration
Semi-active suspension control is able to regulate the damping forces by measuring the relative velocity of the wheels respect to the vehicle body. The adoption of linear potentiometers (the most used sensors in racing for linearity and simplicity) reveals to be expensive for mass market and unreliable in the long run. The paper deals with the validation of a soft sensor for the online estimation of the suspension velocity from acceleration signals. Main features of the soft sensor implementation according to different approaches (based on time-domain integration and Artificial Neural Networks respectively) are discussed with reference to the typical requirements for the real-time prediction of the suspension velocity.