Neural Network Model-Based Predictive Control for Multivariable Nonlinear Systems

被引：8
|
作者：
Alamdari, Bahareh Vatankhah [1 ]
Fatehi, Alireza [1 ]
Khaki-Sedigh, Ali [1 ]
机构：
[1] KN Toosi Univ Technol, Dept Elect Engn, Tehran, Iran
来源：
2010 IEEE INTERNATIONAL CONFERENCE ON CONTROL APPLICATIONS | 2010年
关键词：
Nonlinear predictive control; Multi-input multi-output; MLP neural network; Disturbance rejection; Quadruple tank process;
D O I：
10.1109/CCA.2010.5611265
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A nonlinear model predictive control (NMPC) algorithm based on a neural network model is proposed for multivariable nonlinear systems. A multi-input multi-output model is developed using multilayer perceptron (MLP) neural network which is trained by Levenberg-Marquardt algorithm and amplitude modulated pseudo random binary (APRBS) signals with noise as data sets. Model predictive control also uses Levenberg-Marquardt algorithm for the control signal optimization. The control performance is improved by using a disturbance model that compensates both model mismatch and external disturbance. The learning rate of disturbance estimation network changes adaptively to treat the model mismatch differently from the external disturbance. Simulation results using the quadruple-tank are employed to show the effectiveness of the method.
引用
收藏
页码：920 / 925
页数：6
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