一种新的氧化铝质量分数建模与控制策略

摘要

Considering the problem of alumina concentration modeling and control, a novel modeling and control strategy based on least squares support vector machine (LS-SVM) and predictive control were proposed. First, aiming at the problem of parameter selection of LS-SVM, a CHAOS LS-SVM algorithm based on chaos optimization was presented to obtain optimal alumina concentration prediction model. Then, an alumina concentration predictive control algorithm based on LS-SVM was developed, which uses chaos optimization to solve optimal control law online. The simulation results show that the generalization ability of alumina concentration prediction model established by CHAOS LS-SVM algorithm is stronger than that of alumina concentration prediction model based on neural network (NN), and the control precision of alumina concentration predictive control algorithm based on LS-SVM is higher than that of alumina concentration predictive control algorithm based on NN.%针对氧化铝质量分数的建模与控制问题,提出一种新的基于最小二乘支持向量机(LS-SVM)和预测控制的建模与控制策略.首先,针对LS-SVM建模时的参数选取问题,提出一种基于混沌优化的CHAOS LS-SVM算法获得最优氧化铝质量分数预测模型.然后,提出一种基于LS-SVM的氧化铝质量分数预测控制算法,采用混沌优化在线求解最优控制律.仿真结果表明:CHAOS LS-SVM算法建立的氧化铝质量分数预测模型,其泛化能力要比基于神经网络(NN)的氧化铝质量分数预测模型的强；基于LS-SVM的氧化铝质量分数预测控制算法,其控制精度要比基于NN的氧化铝质量分数预测控制算法的高.