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.
Highlights
Linear control methods are based on the existence of an analytical model of the system
The strong learning capability of the dynamic neural network in identification and control is combined with the functionality of the approximate model controller structure to propose a novel online neural networks (NN)-based controller for nonlinear single-input single-output (SISO) dynamical systems
The results indicate that the online nonlinear autoregressive moving average (NARMA)-L2, nonlinear autoregressive with exogenous input (NARX), and feedforward neural network (FFNN) controllers attain good modeling and control performances
Summary
Linear control methods are based on the existence of an analytical model of the system. Most physical systems have nonlinearity, and their mathematical model is unknown or partially known and variable in time. Artificial neural networks have been effectively used as tracking controllers for unknown linear and nonlinear dynamic plants [6, 7]. It has been shown that ANNS can efficiently approximate dynamics without requiring detailed knowledge of the plant [8, 9]. Another advantage of ANNs is their possibility of learning, which can reduce the human effort during the design of the controllers and allows discovering more effective control structures than those already known
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