基于最近邻聚类支持向量机辨识的电弧炉电极逆控制

Inverse control for electrodes in electric arc furnace based on support-vector-machines identification on nearest neighbor clustering

中文关键词  α阶时延逆系统  伪线性化解耦  支持向量机  最近邻聚类  逆控制  电弧炉电极系统

英文关键词  αth-order time-delay inverse system  pseudo-linear decoupling  support-vector-machines  nearest neighbor

中文摘要

      基于核函数的支持向量机(support-vector-machines, SVM)与三层神经网络等价关系, 构造基于SVM的多变量阶时延逆系统实现对原系统的伪线性化解耦, 提出最近邻聚类的SVM模型辨识算法, 设计了一种带前馈的参数自适应PD调节器和SVM逆控制相结合的控制策略. 通过对典型的MIMO离散非线性可逆系统和电弧炉电极系统的仿真研究, 表明该控制策略对于数学模型未知的不确定系统, 只需要一定量的输入输出数据作为样本学习, 就可实现对系统逆模型的高精度逼近, 控制系统具有良好的动态响应和跟踪精度. 当模型严重不确定、参数摄动、有外界干扰时, 系统具有很好的抗干扰能力和鲁棒性.

英文摘要

      Based on the equivalency between the support-vector-machines(SVM) with kernel functions and the threelayer feedforward neural network, we use support vector machines to build a multi-variable ®th-order time-delay inverse system which realizes the pseudo-linear decoupling for the original system. A SVM model identification algorithm on nearest neighbor clustering is proposed; and the control strategy is designed which combines the feedforward self-tuningparameter PD regulator with the inverse control based on SVM. Through the simulation research on the typical MIMO discrete nonlinear invertible system and the electrode system of the electric arc furnace, we find that the control strategy does not require the a priori knowledge of the mathematical model. Only a small number of input and output data in the sample learning process are sufficient to achieved a high-precision inverse system model. The control system has desirable characteristics of dynamic response and tracking accuracy. The model is highly robust to serious uncertainties, parameters perturbations, and outside interferences.