[1]ZHANG Junling,CHEN Zengqiang,ZHANG Qing.Elman model-free control method based on particle swarm optimization algorithm[J].CAAI Transactions on Intelligent Systems,2016,11(1):49-54.[doi:10.11992/tis.201507025]
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Elman model-free control method based on particle swarm optimization algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 11 Number of periods: 2016 1 Page number: 49-54 Column: 学术论文—智能系统 Public date: 2016-02-25
Title:
Elman model-free control method based on particle swarm optimization algorithm
Author(s):
ZHANG Junling1;  CHEN Zengqiang1; 2;  ZHANG Qing2
1. College of Computer and Control Engineering, Nankai University, Tianjin 300071, China;
2. College of Science, Civil Aviation University of China, Tianjin 300300, China
Keywords:
nonlinear system; discrete nonlinear system; model-free control; controller; Elman neural network; particle swarm optimization algorithm
CLC:
TP18;TP301.6
DOI:
10.11992/tis.201507025
Abstract:
In this paper, we propose amodel-free control method, based on the Elman neural network and the particle swarm optimization algorithm, for a class of single-input single-output (SISO) discrete nonlinear systems, whose mathematical model cannot be established or is not easily modeled. In the model-free control system, it is not necessary to establish a mathematical model for each object. The Elman neural network is the controller and all the online weight parameters are learned using the particle swarm optimization algorithm.Using the proposed method, we obtain the optimal control variable at each discrete time.Them odel-free control method simulation results demonstrate that the nonlinear system output signal has a fast response rate and few tracking errors. Moreover, the control variable has good convergence and high control accuracy. These results prove that the proposed method is reasonable and effective.
References:
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