Kalman filtering and neural networks pdf

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kalman filtering and neural networks pdf

Kalman Filtering and Neural Networks - Semantic Scholar

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. 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. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Kalman Filter Explained

Learning algorithms for neural networks with the Kalman filters

Skip to search form Skip to main content. This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear. The book deals with important applications in such fields as control, financial forecasting, and idle speed control. View via Publisher. Alternate Sources. Save to Library.

Journal of Intelligent and Robotic Systems. Based on various approaches, several different learing algorithms have been given in the literature for neural networks. Almost all algorithms have constant learning rates or constant accelerative parameters, though they have been shown to be effective for some practical applications. The learning procedure of neural networks can be regarded as a problem of estimating or identifying constant parameters i. Making use of the Kalman filtering, we derive a new back-propagation algorithm whose learning rate is computed by a time-varying Riccati difference equation.

Conceived and designed the experiments: YW. Performed the experiments: Z. Analyzed the data: Z. Wrote the paper: Z. 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.

From the Publisher: Kalman filtering is a well-established topic in the field of control and signal processing and represents by far the most refined method for the.
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This paper presents an identification method of dynamic systems based on a group method of data handling approach. In particular, a new structure of the dynamic multi-input multi-output neuron in a state-space representation is proposed. -

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1 thoughts on “Unscented Kalman Filter-Trained Neural Networks for Slip Model Prediction

  1. Kalman filter theory applied to the training and use of neural networks, and some PDF Fellowship and a grant from the Natural Sciences and Engineering.

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