Wednesday , April 24 2024

Recurrent Neural Networks in Linear Systems Controlling

1 Valahia University of Târgovişte,
18-24, Unirii Ave., Târgovişte, Romania,

2Laboratoire d’Automatique du CNAM,
21, Rue Pinel, 75013 Paris, France

Abstract: This paper presents an application of an ANN (Artificial Neural Network) of a RNRF type (Recurrent Network with Radial basis Function) in controlling a linear system. The performance of ANN-based control solution is compared with a classic controller and the results show that ANN behaves better than the classic controller. MATLAB simulation performed show that the coupling between the ANN and a proportional controller gives the best performance.

Keywords: Human-machine interface, usability criteria, end-user development, risks.

>Full text
P. C. PATIC, R. M. ZEMOURI R., L. DUŢĂ, Recurrent Neural Networks in Linear Systems Controlling, Studies in Informatics and Control, ISSN 1220-1766, vol. 19 (2), pp. 153-158, 2010.

1. Introduction

The linear systems control is today an important research and development area in control engineering. In the real world, however, many processes are characterized by a nonlinear dynamic behavior which makes impossible to use conventional tools for automatic control. The same applies to systems for which mathematical models are incompletely specified or of a poor quality. There is currently no systematic theory to be applied to control such processes. To solve this problem, one solution is to use a learning phase to identify the process model or controller. The term ‘learning’ is about changing the structure and/or system settings in order to improve its future performance, based on previous experimental observations [1, 4]. Some adaptive controlling methods have been developed, to enable the evolution of controller depending on the task [3, 9]. The structure of the controller being already chosen, those methods allow fixing a number of parameters of this one. If the general principle used by these algorithms is similar to learning done by ANN, adaptation is done by a simple setting of a small number of coefficients of the control loop without storage capacity [5]. Systems with learning characteristics such as ANN (Artificial Neural Networks) can be successfully utilized in control problems such as the decision support or situation recognition [4, 6, 12, 13]. In this case we speak about learning in command rather than adaptive control.

This article consists of three parts. Section I reviews a dynamic variation of ANN that one of the authors of this paper proposed in a previous work [21]. Section II presents the basic principles and some methods of neurons control technique. Finally, Section III presents the tests and the obtained experimental results.


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