Work

Adaptive Control Using Neural Networks and Approximate Models for Nonlinear Dynamic Systems

Year: 2020

Type: article

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.

Source: Modelling and Simulation in Engineering

DOI: 10.1155/2020/8642915

Authors: Khadija El Hamidi, Mostafa Mjahed, Abdeljalil El Kari, Hassan Ayad

Institutions: Cadi Ayyad University, École Royale de l'Air de Marrakech

Language: English

Topic: Adaptive Control of Nonlinear Systems

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,000

OpenAlex ID: W3080244845