An incremental constructive layer algorithm for controller design

Abstract

In this paper, a systematic approach for Neural Network (NN) controller design based on an incremental constructive layer algorithm is presented. The algorithm starts by considering minimal nodes in the hidden layer and choosing a pattern from those available for initial training. Further training continues with the remaining patterns resulting in a progressive increase in the number of neurons. The proposed design is carried out in three phases, namely, training, validation and pruning. A modified Goodness Factor is proposed in the paper to aid the pruning process. Simulation studies are performed on a single link robot arm. A model reference based NN controller with minimal numbers of nodes is obtained by maintaining the system error tolerance below a specified limit. Time responses obtained for the plant output and the reference signal show a satisfactory performance.