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

Authors:
M. H. Shaheed;M. O. Tokhi
Affiliations:
Department of Engineering, Queen Mary University of London, London (UK);Department of Automatic Control and Systems Engineering, The University of Sheffield, Mapplin Street, Sheffield S1 3JD (UK) o.tokhi@sheffield.ac.uk
Venue:
Robotica
Year:
2002

Citing 7
Cited 5

Adaptation in natural and artificial systems

Adaptation in natural and artificial systems
First- and second-order methods for learning: between steepest descent and Newton's method

Neural Computation
Nonlinear modeling and prediction by successive approximation using radial basis functions

Physica D
Artificial Neural Networks: A Tutorial

Computer - Special issue: neural computing: companion issue to Spring 1996 IEEE Computational Science & Engineering
Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Neuro-Control and Its Applications

Neuro-Control and Its Applications
Applied Neural Networks for Signal Processing

Applied Neural Networks for Signal Processing

A novel ultra-precision integrated robotic system

Robotica
Control of a flexible plate structure using particle swarm optimization

CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Soft computing-based active vibration control of a flexible structure

Engineering Applications of Artificial Intelligence
Non-parametric modelling of a rectangular flexible plate structure

Engineering Applications of Artificial Intelligence
Parametric modelling approach using bacterial foraging algorithms for modelling of flexible manipulator systems

Engineering Applications of Artificial Intelligence

Quantified Score

Hi-index	0.00

Visualization

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.