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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 2/2018

17.11.2016 | Original Article

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

verfasst von: Kerui Xia, Haibo Gao, Liang Ding, Guangjun Liu, Zongquan Deng, Zhen Liu, Changyou Ma

Erschienen in: Neural Computing and Applications | Ausgabe 2/2018

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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.
Vorheriger Artikel Peristaltic flow of MHD Jeffery nanofluid in curved channel with convective boundary conditions: a numerical study
Nächster Artikel Nuclear norm regularized convolutional Max Pos@Top machine

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Metadaten
Titel
Trajectory tracking control of wheeled mobile manipulator based on fuzzy neural network and extended Kalman filtering
verfasst von
Kerui Xia
Haibo Gao
Liang Ding
Guangjun Liu
Zongquan Deng
Zhen Liu
Changyou Ma
Publikationsdatum
17.11.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 2/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2643-7

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