Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 정정주 | - |
dc.date.accessioned | 2019-10-29T06:17:19Z | - |
dc.date.available | 2019-10-29T06:17:19Z | - |
dc.date.issued | 2019-05 | - |
dc.identifier.citation | 2019 한국자동차공학회 춘계학술대회 , Page. 799-802 | en_US |
dc.identifier.uri | http://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE08747888 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/111633 | - |
dc.description.abstract | This paper presents an recurrent neural network-based model predictive control for an autonomous driving vehicle. Model predictive control is effective in vehicle lateral control but too computationally expensive to be applied in real-time control. To resolve this problem, we propose a recurrent neural network-based approximate model predictive control. The offline-trained neural network exhibits the ability to model the waypoint tracking system and provided the closed-loop performance. The performance of the approximate recurrent neural network-model predictive control (RNN-MPC) is validated by computational experiments of waypoints tracking control scheme. | en_US |
dc.language.iso | en | en_US |
dc.publisher | 한국자동차공학회 | en_US |
dc.subject | waypoint tracking control | en_US |
dc.subject | model predictive control | en_US |
dc.subject | recurrent neural network | en_US |
dc.subject | vehicle lateral control | en_US |
dc.subject | lane keeping system | en_US |
dc.title | Recurrent Neural Network-Based Model Predictive Control for Waypoint Tracking | en_US |
dc.type | Article | en_US |
dc.relation.page | 799-802 | - |
dc.contributor.googleauthor | Quan, Ying Shuai | - |
dc.contributor.googleauthor | Choi, Woo Young | - |
dc.contributor.googleauthor | Lee, Seung-Hi | - |
dc.contributor.googleauthor | Chung, Chung Choo | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DIVISION OF ELECTRICAL AND BIOMEDICAL ENGINEERING | - |
dc.identifier.pid | cchung | - |
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