Neural Network Model Predictive Control of Nonlinear Systems Using Genetic Algorithms

被引：0
|
作者：
Rankovic, V. [1 ]
Radulovic, J. [1 ]
Grujovic, N. [1 ]
Divac, D. [2 ]
机构：
[1] Univ Kragujevac, Fac Mech Engn, Dept Appl Mech & Automat Control, Kragujevac 34000, Serbia
[2] Inst Dev Water Resources Jaroslav Cerni, Belgrade 11000, Serbia
来源：
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL | 2012年 / 7卷 / 03期
关键词：
model predictive control; nonlinear system; identification; digital recurrent network; genetic algorithm; IDENTIFICATION;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
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.
引用
收藏
页码：540 / 549
页数：10
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