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Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features
This study firstly analyses the driver’s manipulation behaviour and relates the different components of the driver model. Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving...
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Published in: | Shock and vibration 2018-01, Vol.2018 (2018), p.1-12 |
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container_end_page | 12 |
container_issue | 2018 |
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container_title | Shock and vibration |
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creator | Song, Baoyu Liu, Yiqun Li, Jun Li, Weihua Wang, Jianfeng Gao, Haibo |
description | This study firstly analyses the driver’s manipulation behaviour and relates the different components of the driver model. Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving system of an electric vehicle is used to establish the longitudinal speed control model of the driver by using a feedforward-PID feedback control strategy. Our approach is to release the coupling between direction and speed control and build an integrated model that includes the direction and speed for an arbitrary path. Finally, the characteristics of an actual racing track are considered to establish the fastest driver control model. We simulated the typical operating conditions of our driver operation model. The simulation confirmed the effectiveness of the improved predictive point search algorithm and the integrated driver model to control the direction and speed for an arbitrary path. |
doi_str_mv | 10.1155/2018/7487295 |
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Then, a model controlling the driver directions is built according to the prediction-follower theory with the aim of improving the point search algorithm. A model of the driving system of an electric vehicle is used to establish the longitudinal speed control model of the driver by using a feedforward-PID feedback control strategy. Our approach is to release the coupling between direction and speed control and build an integrated model that includes the direction and speed for an arbitrary path. Finally, the characteristics of an actual racing track are considered to establish the fastest driver control model. We simulated the typical operating conditions of our driver operation model. The simulation confirmed the effectiveness of the improved predictive point search algorithm and the integrated driver model to control the direction and speed for an arbitrary path.</description><identifier>ISSN: 1070-9622</identifier><identifier>EISSN: 1875-9203</identifier><identifier>DOI: 10.1155/2018/7487295</identifier><language>eng</language><publisher>Cairo, Egypt: Hindawi Publishing Corporation</publisher><subject>Computer simulation ; Control theory ; Controllers ; Decision making ; Feedback control ; Feedforward control ; Kalman filters ; Mathematical models ; Mechanical engineering ; Neural networks ; Predictive control ; Proportional integral derivative ; Racing ; Search algorithms ; Sensors ; Speed control ; Vehicles ; Velocity</subject><ispartof>Shock and vibration, 2018-01, Vol.2018 (2018), p.1-12</ispartof><rights>Copyright © 2018 Jianfeng Wang et al.</rights><rights>COPYRIGHT 2018 John Wiley & Sons, Inc.</rights><rights>Copyright © 2018 Jianfeng Wang et al.; This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c465t-cabdb807e4879fc5b7fc34f98e7934baaf292f88f5078725d2020021a20c971a3</citedby><cites>FETCH-LOGICAL-c465t-cabdb807e4879fc5b7fc34f98e7934baaf292f88f5078725d2020021a20c971a3</cites><orcidid>0000-0002-9745-4150 ; 0000-0002-9337-3644 ; 0000-0002-7195-3146</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2041576859/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2041576859?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,25728,27898,27899,36986,44563,75093</link.rule.ids></links><search><contributor>Scott-Emuakpor, Onome E.</contributor><creatorcontrib>Song, Baoyu</creatorcontrib><creatorcontrib>Liu, Yiqun</creatorcontrib><creatorcontrib>Li, Jun</creatorcontrib><creatorcontrib>Li, Weihua</creatorcontrib><creatorcontrib>Wang, Jianfeng</creatorcontrib><creatorcontrib>Gao, Haibo</creatorcontrib><title>Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features</title><title>Shock and vibration</title><description>This study firstly analyses the driver’s manipulation behaviour and relates the different components of the driver model. 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subjects | Computer simulation Control theory Controllers Decision making Feedback control Feedforward control Kalman filters Mathematical models Mechanical engineering Neural networks Predictive control Proportional integral derivative Racing Search algorithms Sensors Speed control Vehicles Velocity |
title | Modeling a Driver’s Directional and Longitudinal Speed Control Based on Racing Track Features |
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