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DOI:	10.1371/journal.pone.0158492
论文题名:	Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction
作者:	Zhencai Li; Yang Wang; Zhen Liu
刊名:	PLOS ONE
ISSN:	1932-6203
出版年:	2016
发表日期:	2016-7-28
卷:	11, 期:7
语种:	英语
英文关键词:	Robots ; Kalman filter ; Velocity ; White noise ; Covariance ; Terrain ; Postural control ; Simulation and modeling
英文摘要:	The purpose of this work is to investigate the accurate trajectory tracking control of a wheeled mobile robot (WMR) based on the slip model prediction. Generally, a nonholonomic WMR may increase the slippage risk, when traveling on outdoor unstructured terrain (such as longitudinal and lateral slippage of wheels). In order to control a WMR stably and accurately under the effect of slippage, an unscented Kalman filter and neural networks (NNs) are applied to estimate the slip model in real time. This method exploits the model approximating capabilities of nonlinear state–space NN, and the unscented Kalman filter is used to train NN’s weights online. The slip parameters can be estimated and used to predict the time series of deviation velocity, which can be used to compensate control inputs of a WMR. The results of numerical simulation show that the desired trajectory tracking control can be performed by predicting the nonlinear slip model.
URL:	http://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0158492&type=printable
Citation statistics:
资源类型:	期刊论文
标识符:	http://119.78.100.158/handle/2HF3EXSE/23550
Appears in Collections:	过去全球变化的重建影响、适应和脆弱性科学计划与规划气候变化与战略全球变化的国际研究计划气候减缓与适应气候变化事实与影响

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journal.pone.0158492.PDF（7638KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	View Download

作者单位:

State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China;State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China;State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China

Recommended Citation:

Zhencai Li,Yang Wang,Zhen Liu. Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction[J]. PLOS ONE,2016-01-01,11(7)

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