基于MFAPC 的无人驾驶汽车路径跟踪方法
Path Tracking Method of Unmanned Vehicle Based on MFAPC
查看参考文献19篇
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文摘
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为提高无人驾驶汽车转向系统对目标路径的跟踪精度,提出一种新的无人驾驶汽车路径跟踪方法。介绍基于跟踪预瞄点的无人驾驶汽车横向控制方案,给出系统的动态线性化数据模型及其多步预测方程,并采用最小二乘法推导出伪梯度向量的估计和预测方程。结合无模型自适应控制与预测控制的优点,能够通过滚动优化策略进行反复的在线计算,从而得到较好的动态性能。基于CarSim/Simulink 联合仿真平台,在车速分别为5 m/s、20 m/s 时进行验证,结果表明,与基于车辆动力学的MPC 方案相比,该方法具有更好的跟踪效果。 |
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其他语种文摘
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In order to improve the tracking precision of the unmanned vehicle steering system to the target path,this paper proposes a new unmanned vehicle path tracking method.It introduces the lateral control scheme of unmanned vehicle based on tracking preview point,and gives the dynamic linearized data model of the system and its multi-step prediction equation.The estimation and prediction model of pseudo-gradient vectors is derived by using the method of least squares.Adopting the advantages of model-free adaptive predictive control,it can perform repeated online calculations through rolling optimization strategies,so as to obtain better dynamic performance.Based on the CarSim/Simulink co-simulation platform,the method is verified when the vehicle speed is 5 m/s and 20 m/s.Results show that compared with the MPC scheme based on vehicle dynamics,t his proposed method has better tracking performance. |
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来源
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计算机工程
,2019,45(6):6-11,20 【扩展库】
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DOI
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10.19678/j.issn.1000-3428.0052439
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关键词
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无人驾驶
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路径跟踪
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预瞄策略
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无模型自适应预测控制
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联合仿真
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地址
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北京工业大学信息学部, 北京, 100124
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3428 |
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学科
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自动化技术、计算机技术 |
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基金
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北京市属高等学校人才强教计划项目
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文献收藏号
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CSCD:6513195
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参考文献 共
19
共1页
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|
1.
Samuel M. A review of some pure-pursuit based path tracking techniques for control of autonomous vehicle.
International Journal of Computer Applications,2016,135(1):35-38
|
被引
9
次
|
|
|
|
|
2.
陈虹. 汽车控制的研究现状与展望.
自动化学报,2013,39(4):322-346
|
被引
33
次
|
|
|
|
|
3.
陈慧岩. 智能汽车横向控制方法研究综述.
兵工学报,2017,38(6):1203-1214
|
被引
32
次
|
|
|
|
|
4.
Menhour L. An efficient model-free setting for longitudinal and lateral vehicle control: validation through the interconnected pro-SiVIC/RTMaps prototyping platform.
IEEE Transactions on Intelligent Transportation Systems,2017(99):1-15
|
被引
1
次
|
|
|
|
|
5.
Fachrudin F. Optimization of automatic steering control on vehicle with steer by wire system using particle swarm optimization.
Turkish Journal of Electrical Engineering and Computer Sciences,2016,24(1):541-557
|
被引
2
次
|
|
|
|
|
6.
龚建伟.
无人驾驶车辆模型预测控制,2014
|
被引
85
次
|
|
|
|
|
7.
侯忠生. 再论无模型自适应控制.
系统科学与数学,2014,34(10):1182-1191
|
被引
20
次
|
|
|
|
|
8.
Xu Dezhi. A novel model-free adaptive control design for multi-variable industrial processes.
IEEE Transactions on Industrial Electronics,2014,61(11):6391-6398
|
被引
5
次
|
|
|
|
|
9.
Hou Zhongsheng. Data-driven model-free adaptive control for a class of MIMO nonlinear discretetime systems.
IEEE Transactions on Neural Networks,2011,22(12):2173-2188
|
被引
39
次
|
|
|
|
|
10.
侯忠生.
无模型自适应控制,2013
|
被引
6
次
|
|
|
|
|
11.
He Lihong. Research on application of buck converter based on datadriven model-free adaptive controller.
Applied Mechanics and Materials,2013,427:1044-1047
|
被引
1
次
|
|
|
|
|
12.
Huang Zhanjun. Boost converter optimal control based on MFAC and FPSOA under model mismatch.
Proceedings of IEEE CCDC'16,2016:5778-5783
|
被引
1
次
|
|
|
|
|
13.
Li Haitao. Implementation of a MFAC based position sensorless drive for high speed BLDC motors with nonideal back EMF.
ISA Transactions,2016,67:348-355
|
被引
1
次
|
|
|
|
|
14.
Sha Liming. Research on optimized control technology of permanent magnet linear motor based on MFAC.
Journal of Xinyu University,2017(2):123-130
|
被引
1
次
|
|
|
|
|
15.
Yao Wenlong. Design of vector control based on MFAC for SSP podded propulsion.
Proceedings of IEEE International Conference on Robotics and Biomimetics,2014:2418-2423
|
被引
1
次
|
|
|
|
|
16.
侯忠生. 基于坐标补偿的自动泊车系统无模型自适应控制.
自动化学报,2015,41(4):823-831
|
被引
21
次
|
|
|
|
|
17.
田涛涛. 基于无模型自适应控制的无人驾驶汽车横向控制方法.
自动化学报,2017,43(11):1931-1940
|
被引
26
次
|
|
|
|
|
18.
席裕庚.
预测控制. (2版),2013
|
被引
18
次
|
|
|
|
|
19.
段建民. 基于Pure Pursuit算法的智能车路径跟踪.
北京工业大学学报,2016,42(9):1301-1306
|
被引
15
次
|
|
|
|
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