{"title":"考虑转向滞后和车辆与路面状态的无人驾驶采矿卡车侧向控制","authors":"Qiushi Chen, Guangqiang Wu, Qi Zeng, Jianzhuang Zong","doi":"10.4271/02-17-01-0004","DOIUrl":null,"url":null,"abstract":"Lateral control is an essential part of driverless mining truck systems. However,\n the considerable steering lag and poor tracking accuracy limit the development\n of unmanned mining. In this article, a dynamic preview distance was designed to\n resist the steering lag. Then the vehicle–road states, which described the\n real-time lateral and heading errors between the vehicle and the target road,\n was defined to describe the control strategy more efficiently. In order to trade\n off the tracking accuracy and stability, the Takagi–Sugeno (TS) fuzzy method was\n used to adjust the weight matrix of the linear quadratic regulator (LQR) for\n different vehicle–road states. Based on the actual mine production environment\n and the TR100 mining truck, experimental results show that the TS-LQR algorithm\n performed much better than the pure pursuit algorithm.","PeriodicalId":45281,"journal":{"name":"SAE International Journal of Commercial Vehicles","volume":null,"pages":null},"PeriodicalIF":0.6000,"publicationDate":"2023-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Lateral Control for Driverless Mining Trucks with the Consideration\\n of Steering Lag and Vehicle–Road States\",\"authors\":\"Qiushi Chen, Guangqiang Wu, Qi Zeng, Jianzhuang Zong\",\"doi\":\"10.4271/02-17-01-0004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Lateral control is an essential part of driverless mining truck systems. However,\\n the considerable steering lag and poor tracking accuracy limit the development\\n of unmanned mining. In this article, a dynamic preview distance was designed to\\n resist the steering lag. Then the vehicle–road states, which described the\\n real-time lateral and heading errors between the vehicle and the target road,\\n was defined to describe the control strategy more efficiently. In order to trade\\n off the tracking accuracy and stability, the Takagi–Sugeno (TS) fuzzy method was\\n used to adjust the weight matrix of the linear quadratic regulator (LQR) for\\n different vehicle–road states. Based on the actual mine production environment\\n and the TR100 mining truck, experimental results show that the TS-LQR algorithm\\n performed much better than the pure pursuit algorithm.\",\"PeriodicalId\":45281,\"journal\":{\"name\":\"SAE International Journal of Commercial Vehicles\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-12-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAE International Journal of Commercial Vehicles\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4271/02-17-01-0004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAE International Journal of Commercial Vehicles","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/02-17-01-0004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Lateral Control for Driverless Mining Trucks with the Consideration
of Steering Lag and Vehicle–Road States
Lateral control is an essential part of driverless mining truck systems. However,
the considerable steering lag and poor tracking accuracy limit the development
of unmanned mining. In this article, a dynamic preview distance was designed to
resist the steering lag. Then the vehicle–road states, which described the
real-time lateral and heading errors between the vehicle and the target road,
was defined to describe the control strategy more efficiently. In order to trade
off the tracking accuracy and stability, the Takagi–Sugeno (TS) fuzzy method was
used to adjust the weight matrix of the linear quadratic regulator (LQR) for
different vehicle–road states. Based on the actual mine production environment
and the TR100 mining truck, experimental results show that the TS-LQR algorithm
performed much better than the pure pursuit algorithm.