Friday, February 19, 2010
AN INTRODUCTION TO THE USEOF NEURAL NETWORKS IN CONTROL SYSTEMS
The purpose of this paper is to provide a quick overview of neural networks and to explain how they can be used in control systems. We introduce the multilayer perceptron neural network and describe how it can be used for function approximation. The backpropagation algorithm (including its variations) is the principal procedure for training multilayer perceptrons; it is briefly described here. Care must be taken, when training perceptron networks, to ensure that they do not overfit the training data and then fail to generalize well in new situations. Several techniques for improving generalization are discussed. The paper also presents three control architectures: model reference adaptive control, model predictive control, and feedback linearization control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks. We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system.
Application - Magnetic Levitation System
Now we will demonstrate the predictive controller by applying it to a simple test problem. In this test problem, the objective is to control the position of a magnet suspended above an electromagnet, where the magnet is constrained so that it can only move in the vertical direction, as shown in Figure
Magnetic Levitation System
NEURAL NETWORK-BASED ROBUST TRACKING CONTROL FOR MAGNETIC LEVITATION SYSTEM
This paper proposes a robust tracking controller with bound estimation based on neural network for the magnetic levitation system. The neural network is to approximate an unknown uncertain nonlinear dynamic function in the model of the magnetic levitation system. And the robust control is proposed to compensate for approximation error from the neural network. The weights of the neural network are tuned on-line and the bound of the approximation error is estimated by the adaptive law. The stability of the proposed controller is proven by Lyapunop theory. The robustness effect of the proposed controller is verified by the simulation and experimental results for the magnetic levitation system.
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