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

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 no...

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Bibliographic Details
Published in:Modelling and simulation in engineering 2020, Vol.2020, p.1-13
Main Authors: El Hamidi, Khadija, Mjahed, Mostafa, El Kari, Abdeljalil, Ayad, Hassan
Format: Article
Language:English
Subjects:
Adaptive control
Algorithms
Artificial neural networks
Autoregressive moving average
Back propagation
Back propagation networks
Comparative studies
Controllers
Dynamical systems
Mathematical models
Multilayers
Neural networks
Neurons
Nonlinear control
Nonlinear dynamics
Nonlinear systems
Recurrent neural networks
Stability
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Summary: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.
ISSN:1687-5591
1687-5605
DOI:10.1155/2020/8642915