Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems. (26th August 2020)

• Record Type: Journal Article
• Authors: El Hamidi, Khadija; Mjahed, Mostafa; El Kari, Abdeljalil; Ayad, Hassan
• Other Names: Calì Michele Academic Editor.
• Is Part Of: Modelling and simulation in engineering. Volume 2020(2020)
• Publication Date: 2020-08-26
• Subjects: Engineering -- Simulation methods -- Periodicals; Engineering -- Mathematical models -- Periodicals; 620.004
• DOI: 10.1155/2020/8642915
• Languages: English
• ISSNs: 1687-5591

Abstract

Abstract : In this research, a comparative study of two recurrent neural networks, nonlinear autoregressive with exogenous input (NARX) neural network and nonlinear autoregressive moving average (NARMA-L2), and a feedforward neural network (FFNN) is performed for their ability to provide adaptive control of nonlinear systems. Three dynamical nonlinear systems of different complexity are considered. The aim of this work is to make the output of the plant follow the desired reference trajectory. The problem becomes more challenging when the dynamics of the plants are assumed to be unknown, and to tackle this problem, a multilayer neural network-based approximate model is set up which will work in parallel to the plant and the control scheme. The network parameters are updated using the dynamic backpropagation (BP) algorithm.