车辆关键状态的平行估计
汪1,2; 林懿伦3,4; 戴星原3,4; 项韦杰5
Source Publication指挥与控制学报
2017
Volume3Issue:3Pages:186-194
Other Abstract
 
车辆关键状态的估计问题对实施前馈控制、提高车辆行驶的安全性至关重要.一直以来,受限于车载设备功能与算力、传感器性能等原因,对于难测状态的估计均采用了模型驱动的方法,基于简化的车辆动态模型实施估计.平行估计是一种新的估计方法,通过引入平行系统与深度神经网络,实现了数据驱动的、可在线学习、动态更新,并具有良好鲁棒性与高数据效率的估计方法.实验表明,该方法对于车辆关键状态的估计具有精度较高,对各类工况均能适用的良好性质.
车辆关键状态的估计问题对实施前馈控制、提高车辆行驶的安全性至关重要.一直以来,受限于车载设备功能与算力、传感器性能等原因,对于难测状态的估计均采用了模型驱动的方法,基于简化的车辆动态模型实施估计.平行估计是一种新的估计方法,通过引入平行系统与深度神经网络,实现了数据驱动的、可在线学习、动态更新,并具有良好鲁棒性与高数据效率的估计方法.实验表明,该方法对于车辆关键状态的估计具有精度较高,对各类工况均能适用的良好性质.; The estimation of critical states of vehicles is critical to the implementation of feedforward control and the safety of the driving vehicle. Limited by the computational power of onboard equipment and the performance of sensors, the model-driven method is generally adopted to estimate states of difficult measurements, and the estimation is carried out based on simplified vehicle dynamic models. We propose a new data-driven method called Parallel Estimation Method. Combining parallel systems and deep neural networks, our method is capable to perform an online learning and to update dynamically, which makes the method more robust and have high data efficiency. Experimental results show that the proposed method has good precision and can be applied to various working conditions.
Keyword深度学习 状态估计 平行系统 平行理论
DOI10.3969/j.issn.2096-0204.2017.03.0186
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20247
Collection复杂系统管理与控制国家重点实验室_先进控制与自动化
Corresponding Author林懿伦
Affiliation1.南京航空航天大学
2.青岛智能产业技术研究院平行驾驶创新技术研发中心
3.中国科学院自动化研究所复杂系统管理与控制国家重点实验室
4.中国科学院大学
5.北京科技大学自动化学院
Recommended Citation
GB/T 7714
汪,林懿伦,戴星原,等. 车辆关键状态的平行估计[J]. 指挥与控制学报,2017,3(3):186-194.
APA 汪,林懿伦,戴星原,&项韦杰.(2017).车辆关键状态的平行估计.指挥与控制学报,3(3),186-194.
MLA 汪,et al."车辆关键状态的平行估计".指挥与控制学报 3.3(2017):186-194.
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