Research on nonlinear predictive control algorithm based on BP-ARMAX combination model

被引：0

作者：

Chen, Bai ^{[1
]}

Li, Zai ^{[1
]}

Zhang, Huihua ^{[1
]}

Sun, Chao ^{[1
]}

Hao, Xiaochen ^{[1
]}

机构：

[1] Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China

来源：

Journal of Computational Information Systems | 2014年 / 10卷 / 07期

关键词：

Least squares approximations - Predictive control systems - Neural networks - Multivariable systems;

D O I：

10.12733/jcis10069

中图分类号：

学科分类号：

摘要：

To solve the noise interference problem of multivariable system, a nonlinear predictive control algorithm based on BP-ARMAX combination model is presented. The algorithm builds the system model by the BP neural network and the recursive extended least square method, the dynamic ARMAX model can describe the characteristic of system noise, therefore the model's accuracy is enforced, and the predictive model can be more close to the actual system. We analysis and simulate the curve tracking features and control accuracy of the predictive control algorithm. Simulation results show that, the combined model predictive control algorithm has perfect control effort. © 2014 Binary Information Press.

引用

页码：3073 / 3080

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