Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

  • Vesna Rankovic Faculty of Mechanical Engineering, University of Kragujevac Department for Applied Mechanics and Automatic Control Serbia, 34000 Kragujevac; Sestre Janji´c 6
  • Jasna Radulovic Faculty of Mechanical Engineering, University of Kragujevac Department for Applied Mechanics and Automatic Control Serbia, 34000 Kragujevac; Sestre Janji´c 6
  • Nenad Grujovic Faculty of Mechanical Engineering, University of Kragujevac Department for Applied Mechanics and Automatic Control Serbia, 34000 Kragujevac; Sestre Janji´c 6
  • Dejan Divac Institute for Development of Water Resources "Jaroslav Cˇ erni" Serbia, 11000 Belgrade Jaroslava Cˇ ernog St., 11226 Beli Potok

Abstract

In this paper the synthesis of the predictive controller for control of the nonlinear object is considered. It is supposed that the object model is not known. The method is based on a digital recurrent network (DRN) model of the system to be controlled, which is used for predicting the future behavior of the output variables. The cost function which minimizes the difference between the future object outputs and the desired values of the outputs is formulated. The function ga of the Matlab’s Genetic Algorithm Optimization Toolbox is used for obtaining the optimum values of the control signals. Controller synthesis is illustrated for plants often referred to in the literature. Results of simulations show effectiveness of the proposed control system.

Author Biography

Vesna Rankovic, Faculty of Mechanical Engineering, University of Kragujevac Department for Applied Mechanics and Automatic Control Serbia, 34000 Kragujevac; Sestre Janji´c 6
Department for Applied Mechanics and Automatic Control

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Published
2014-09-18
How to Cite
RANKOVIC, Vesna et al. Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 7, n. 3, p. 540-549, sep. 2014. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/1394>. Date accessed: 05 aug. 2020. doi: https://doi.org/10.15837/ijccc.2012.3.1394.

Keywords

model predictive control, nonlinear system, identification, digital recurrent network, genetic algorithm