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

Mohammad osman Tokhi, Mohammad Osman

Research output: Contribution to journalArticlepeer-review

43 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)93-109
JournalRobotica
DOIs
Publication statusPublished - 1 Jan 2002

Keywords

  • RLS algorithm
  • dynamic modelling
  • flexible manipulator
  • radial basis function
  • genetic algorithm
  • Backpropagation
  • multi-layered perceptron
  • LMS algorithm
  • NARX model
  • neural networks

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