Artificial neural network-based adaptive control for nonlinear dynamical systems

Abstract

This research article presents an artificial neural network (ANN)-based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN-based controllers is obtained by using the most popular optimization algorithm which is known as the back-propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed-forward neural network (FFNN), and radial basis function network (RBFN)-based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.