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Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering

Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and... For robot trajectory tracking control, it is necessary to model inverse dynamics system sufficiently well to allow high-performance control. However, for complex robots such as wheeled mobile manipulators (WMMs), it is often difficult to model the dynamics system owing to system uncertainties, nonlinearity, and coupling. In this paper, we propose an effective tracking control method based on fuzzy neural network (FNN) and extended Kalman filter (EKF) to achieve WMM followed reference trajectory efficiently. The FNN is trained to generate a feedforward torque. In order to increase the computational efficiency and precision of the training algorithm, the EKF is used to sequentially update both the output weights and centers of the FNN. The effectiveness of the proposed control algorithm is confirmed through system experiments. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neural Computing and Applications Springer Journals

Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering

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References (39)

Publisher
Springer Journals
Copyright
Copyright © 2016 by The Natural Computing Applications Forum
Subject
Computer Science; Artificial Intelligence (incl. Robotics); Data Mining and Knowledge Discovery; Probability and Statistics in Computer Science; Computational Science and Engineering; Image Processing and Computer Vision; Computational Biology/Bioinformatics
ISSN
0941-0643
eISSN
1433-3058
DOI
10.1007/s00521-016-2643-7
Publisher site
See Article on Publisher Site

Abstract

For robot trajectory tracking control, it is necessary to model inverse dynamics system sufficiently well to allow high-performance control. However, for complex robots such as wheeled mobile manipulators (WMMs), it is often difficult to model the dynamics system owing to system uncertainties, nonlinearity, and coupling. In this paper, we propose an effective tracking control method based on fuzzy neural network (FNN) and extended Kalman filter (EKF) to achieve WMM followed reference trajectory efficiently. The FNN is trained to generate a feedforward torque. In order to increase the computational efficiency and precision of the training algorithm, the EKF is used to sequentially update both the output weights and centers of the FNN. The effectiveness of the proposed control algorithm is confirmed through system experiments.

Journal

Neural Computing and ApplicationsSpringer Journals

Published: Nov 17, 2016

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