Direction Control and Speed Control Combined Model of Motor-Imagery Based Brain-Actuated Vehicle
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- 英文论文题名：Direction Control and Speed Control Combined Model of Motor-Imagery Based Brain-Actuated Vehicle
- 论文作者：Guodong Yin ; Lei Gong
- 英文论文作者：Guodong Yin ; Lei Gong ; School of Mechanical Engineering ; Southeast University ; State Key Laboratory of Automotive Simulation and Control ; Jilin University
- 年：2017
- 作者机构：School of Mechanical Engineering,Southeast University;State Key Laboratory of Automotive Simulation and Control,Jilin University;
- 英文论文关键词：motor-imagery ; brain-actuated vehicle ; BCI ; modeling ; steering and speed control
- 会议召开时间：2017-07-26
- 会议录名称：第36届中国控制会议论文集（B）
- 英文会议录名称：Proceedings of the 36th Chinese Control Conference（B）
- 语种：英文
- 分类号：U463.6
- 学会代码：KZLL
- 会议名称：第36届中国控制会议
- 会议地点：中国辽宁大连
- 主办单位：中国自动化学会控制理论专业委员会
- 学会名称：中国自动化学会控制理论专业委员会
- 页数：5
- 文件大小：382k
- 原文格式：D
- 会议级别：全国
摘要
The brain-actuated vehicle model is of great value to develop a brain-actuated assistive driving. Furthermore, this study helps to develop other BCI systems such as mobile robots, wheelchairs and unmanned aerial vehicle. In this paper, we propose a control model of motor-imagery based brain-actuated vehicle. This model includes a direction control and speed control combined driver model, a motor imagery model, a brain-computer interface(BCI) performance model, a control model, a motor model and a vehicle model. The innovations of this paper are as follows: firstly, this model of the brain-actuated vehicle considers direction and speed control simultaneously and their coupling relationship; secondly, this model is based on motor imagery which is more applicable compared with other EEG signals. Simulation results show that the performance of the proposed model is close to expectant results.
The brain-actuated vehicle model is of great value to develop a brain-actuated assistive driving. Furthermore, this study helps to develop other BCI systems such as mobile robots, wheelchairs and unmanned aerial vehicle. In this paper, we propose a control model of motor-imagery based brain-actuated vehicle. This model includes a direction control and speed control combined driver model, a motor imagery model, a brain-computer interface(BCI) performance model, a control model, a motor model and a vehicle model. The innovations of this paper are as follows: firstly, this model of the brain-actuated vehicle considers direction and speed control simultaneously and their coupling relationship; secondly, this model is based on motor imagery which is more applicable compared with other EEG signals. Simulation results show that the performance of the proposed model is close to expectant results.
The brain-actuated vehicle model is of great value to develop a brain-actuated assistive driving. Furthermore, this study helps to develop other BCI systems such as mobile robots, wheelchairs and unmanned aerial vehicle. In this paper, we propose a control model of motor-imagery based brain-actuated vehicle. This model includes a direction control and speed control combined driver model, a motor imagery model, a brain-computer interface(BCI) performance model, a control model, a motor model and a vehicle model. The innovations of this paper are as follows: firstly, this model of the brain-actuated vehicle considers direction and speed control simultaneously and their coupling relationship; secondly, this model is based on motor imagery which is more applicable compared with other EEG signals. Simulation results show that the performance of the proposed model is close to expectant results.
引文
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[16]Pfurtscheller G,Brunner C,Schl?gl A,et al,Mu rhythm(de)synchronization and EEG single-trial classification of different motor imagery tasks,Neuro Image,31(1):153-159,2006.
[17]Rampil I J,A primer for EEG signal processing in anesthesia,The Journal of the American Society of Anesthesiologists,89(4):980-1002,1998.
[18]DING Haitao,GUO Konghui,LI Fei,ZHANG Jianwei,Arbitrary path and speed following driver model based on vehicle acceleration feedback,Journal of Mechanical Engineering,46(10):116-120,2010.
[2]Hochberg L R,Bacher D,Jarosiewicz B,et al,Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,Nature,485(7398):372-375,2012.
[3]Zhang R,Li Y,Yan Y,et al,Control of a wheelchair in an indoor environment based on a brain-computer interface and automated navigation,IEEE Trans.on Neural Systems and Rehabilitation Engineering,24(1):128-139,2016.
[4]Rebsamen B,Guan C,Zhang H,et al,A brain controlled wheelchair to navigate in familiar environments,IEEE Trans.on Neural Systems and Rehabilitation Engineering,18(6):590-598,2010.
[5]Millan J R,Renkens F,Mourino J,et al Noninvasive brain-actuated control of a mobile robot by human EEG,IEEE Trans.on Biomedical Engineering,51(6):1026-1033,2004.
[6]Hochberg L R,Bacher D,Jarosiewicz B,et al,Reach and grasp by people with tetraplegia using a neurally controlled robotic arm,Nature,485(7398):372-375,2012.
[7]Bi L,Fan X,Jie K,et al,Using a head-up display-based steady-state visually evoked potential brain-computer interface to control a simulated vehicle,IEEE Trans.on Intelligent Transportation Systems,15(3):959-966,2014.
[8]Fan X,Bi L,Teng T,et al,A brain-computer interface-based vehicle destination selection system using P300 and SSVEPsignals,IEEE Trans.on Intelligent Transportation Systems,16(1):274-283,2015.
[9]Bradberry T J,Gentili R J,Contreras-Vidal J L,Fast attainment of computer cursor control with noninvasively acquired brain signals,Journal of neural engineering,8(3):036010,2011.
[10]Borgul A,Bazylev D,Brain controlled multiagent aerial vehicles system,in Proceedings of 18th International Conference on Methods and Models in Automation and Robotics(MMAR),2013:736-741.
[11]Shi T,Wang H,Zhang C,Brain Computer Interface system based on indoor semi-autonomous navigation and motor imagery for Unmanned Aerial Vehicle control,Expert Systems with Applications,42(9):4196-4206,2015.
[12]G?hring D,Latotzky D,Wang M,et al,Semi-autonomous car control using brain computer interfaces,Intelligent Autonomous Systems 12,Springer-Berlin Heidelberg,2013:393-408.
[13]Bi L,Lu Y,Fan X,et al,Queuing Network Modeling of driver EEG signals-based steering control,IEEETransactions on Neural Systems&Rehabilitation Engineering,PP(99):1-1,2016.
[14]Pl?chl M,Edelmann J,Driver models in automobile dynamics application,Vehicle System Dynamics,45(7-8):699-741,2007.
[15]GUO Konghui,DING Haitao,ZHANG Jianwei,et al,Development of a longitudinal and lateral driver model for autonomous vehicle control,International Journal of Vehicle Design,36(1):50-65,2004.
[16]Pfurtscheller G,Brunner C,Schl?gl A,et al,Mu rhythm(de)synchronization and EEG single-trial classification of different motor imagery tasks,Neuro Image,31(1):153-159,2006.
[17]Rampil I J,A primer for EEG signal processing in anesthesia,The Journal of the American Society of Anesthesiologists,89(4):980-1002,1998.
[18]DING Haitao,GUO Konghui,LI Fei,ZHANG Jianwei,Arbitrary path and speed following driver model based on vehicle acceleration feedback,Journal of Mechanical Engineering,46(10):116-120,2010.