Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction.
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 slipp...
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doaj-70bd653e1ca4420cb0153d2262b071172020-11-24T22:12:25ZengPublic Library of Science (PLoS)PLoS ONE1932-62032016-01-01117e015849210.1371/journal.pone.0158492Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction.Zhencai LiYang WangZhen LiuThe 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.http://europepmc.org/articles/PMC4965072?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zhencai Li Yang Wang Zhen Liu |
spellingShingle |
Zhencai Li Yang Wang Zhen Liu Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction. PLoS ONE |
author_facet |
Zhencai Li Yang Wang Zhen Liu |
author_sort |
Zhencai Li |
title |
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction. |
title_short |
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction. |
title_full |
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction. |
title_fullStr |
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction. |
title_full_unstemmed |
Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction. |
title_sort |
unscented kalman filter-trained neural networks for slip model prediction. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS ONE |
issn |
1932-6203 |
publishDate |
2016-01-01 |
description |
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://europepmc.org/articles/PMC4965072?pdf=render |
work_keys_str_mv |
AT zhencaili unscentedkalmanfiltertrainedneuralnetworksforslipmodelprediction AT yangwang unscentedkalmanfiltertrainedneuralnetworksforslipmodelprediction AT zhenliu unscentedkalmanfiltertrainedneuralnetworksforslipmodelprediction |
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1725803817481535488 |