A Radial Basis Function Neural Network Model Reference Adaptive Controller for Nonlinear Systems

被引：0
|
作者：
Slema, Sabrine [1 ,2 ]
Errachdi, Ayachi [1 ,2 ]
Benrejeb, Mohamed [1 ,2 ]
机构：
[1] Tunis El Manar Univ, Natl Engn Sch Tunis, Dept Elect Engn, Belvedere BP 37, Tunis 1002, Tunisia
[2] Tunis El Manar Univ, Natl Engn Sch Tunis, Automat Res Lab, Belvedere BP 37, Tunis 1002, Tunisia
来源：
2018 15TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS AND DEVICES (SSD) | 2018年
关键词：
Model reference adaptive controller; Radial Basis Function neural network (RBF NN); nonlinear systems;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, an algorithm of a model reference adaptive controller for nonlinear systems based on Radial Basis Function (RBF) Neural Networks (NN) is proposed. An adaptive RBF NN is trained using the error between the system response and the desired response as given by the RBF NN reference model. The stable controller-parameters adjustment mechanism is based on the convergence of the synaptic weights, centers and widths of the RBF network. Tuning the parameters of the RBF neural network is performed such that control error reduction and improved tracking accuracy are accomplished. The control signal obtained can make the real system close to the reference model. The identification and control error, with acceptable convergence speed, have been achieved in the case of third-order non-linearity.
引用
收藏
页码：958 / 964
页数：7
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