Modelling of a magneto-rheological damper by evolving radial basis function networks

Authors:
Haiping Du;James Lam;Nong Zhang
Affiliations:
Control and Power Group, Department of Electrical & Electronic Engineering, Imperial College London, Exhibition Road, London SW7 2AZ;Department of Mechanical Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong;Faculty of Engineering, University of Technology, Sydney
Venue:
Engineering Applications of Artificial Intelligence
Year:
2006

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Abstract

This paper presents an approach to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper using evolving radial basis function (RBF) networks. Due to the highly nonlinear characteristics of MR dampers, modelling of MR dampers becomes a very important problem to their applications. In this paper, an alternative representation of the MR damper in terms of evolving RBF networks, which have a structure of four input neurons and one output neuron to emulate the forward and inverse dynamic behaviours of an MR damper, respectively, is developed by combining the genetic algorithms (GAs) to search for the network centres with other standard learning algorithms. Training and validating of the evolving RBF network models are achieved by using the data generated from the numerical simulation of the nonlinear differential equations proposed for the MR damper. It is shown by the validation tests that the evolving RBF networks can represent both forward and inverse dynamic behaviours of the MR damper satisfactorily.