Online MRAC method using neural networks based on variable learning rate for non-linear systems

Abstract

This paper presents a new algorithm of a neural network model reference adaptive controller that uses a variable learning rate. It illustrates how the learning rate affects directly training speed. The neural network training algorithms, such as backpropagation, suffer from low-convergence speed as time-consuming. While increasing the learning rate may help to proceed much faster, it can result in unstable training in terms of weights divergence. Therefore, we propose a neural controller training algorithm using a variable learning rate which is capable of speeding up the learning process significantly and it can provide simultaneously stability of the learning process. The results of simulation show that using variable learning rate has better effects both on response time and on tracking performance.