Open AccessResearch on a proportional-integral-derivative neural network decoupling control based on genetic algorithm optimization for unified chaotic system
Author(s) -
Peifeng Niu,
Jun Zhang,
Xinping Guan
Publication year - 2007
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.56.2493
Subject(s) - pid controller , decoupling (probability) , control theory (sociology) , computer science , chaotic , genetic algorithm , nonlinear system , convergence (economics) , artificial neural network , algorithm , control (management) , temperature control , control engineering , physics , artificial intelligence , engineering , quantum mechanics , machine learning , economics , economic growth
An improved genetic algorithm (IGA) was proposed. It can optimize the proportional-integral-derivative(PID) neural network decoupling controller's connecting weight value, so that it makes the PID controller's parameser to be optimized and realizes the decoupling control of multivariate nonlinearity systems. The IGA is superior to the elementary genetic algorithm. In the PID controller's parameter optimization, the IGA uses less calculations, is more efficient, and faster in convergence. When the optimized PID controller was applied to unified chaoticsystems, good control results were obtained by simulation experimentation, so t was proved that the PID controller when applied to unified chaotic systems wa effective.