Neural network modelling

Abstract

This chapter presents the neural network modelling of a single link flexible manipulator system. The modelling exercise is presented in two parts. The first part of the chapter uses two popular neural network structure, multilayer perceptron (MLP) and radial basis function (RBF). After appropriate training these are used to identify the dominant vibration modes of a flexible manipulator system. The system identification is realised by minimising the prediction error of the actual plant output and the model output. The second part of the chapter deals with the neural network modelling of dynamic systems. This is to perform parametric identification of a physical system and identify structural features and parameter values including the identification of the model structure. The neural network trained through supervised learning is used for both structure identification and parameter estimation. The technique is then used to model a flexible manipulator system using a composite input torque. The models are developed for hub angle, hub velocity and end-point acceleration.