Dynamic modelling of a single-link flexible manipulator: parametric and non-parametric approaches

This paper presents an investigation into the development of parametric and non-parametric approaches for dynamic modelling of a flexible manipulator system. The least mean squares, recursive least squares and genetic algorithms are used to obtain linear parametric models of the system. Moreover, no...

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Bibliographic Details
Published in:Robotica 2002-01, Vol.20 (1), p.93-109
Main Authors: Shaheed, M.H., Tokhi, M.O.
Format: Article
Language:English
Subjects:
Backpropagation
Dynamic modelling
Flexible manipulator
Genetic algorithm
LMS algorithm
Multi-layered perceptron
NARX model
Neural networks
Radial basis function
RLS algorithm
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Summary:This paper presents an investigation into the development of parametric and non-parametric approaches for dynamic modelling of a flexible manipulator system. The least mean squares, recursive least squares and genetic algorithms are used to obtain linear parametric models of the system. Moreover, non-parametric models of the system are developed using a non-linear AutoRegressive process with eXogeneous input model structure with multi-layered perceptron and radial basis function neural networks. The system is in each case modelled from the input torque to hub-angle, hub-velocity and end-point acceleration outputs. The models are validated using several validation tests. Finally, a comparative assessment of the approaches used is presented and discussed in terms of accuracy, efficiency and estimation of the vibration modes of the system.
ISSN:0263-5747
1469-8668
DOI:10.1017/S0263574701003678