Research of wheat dampening MPC system based on RBF neural network algorithm

Author

Zhang Yingjie ; Wang Kai ; Ge LuSheng ; Zhang Qingfeng

Author_Institution

Sch. of Electr. Eng. & Inf., Anhui Univ. of Technol., Ma´anshan, China

fYear

2013

fDate

25-27 May 2013

Firstpage

5099

Lastpage

5102

Abstract

For the nonlinear time delay features that exists in the wheat watering system of flour production industrial processes control, according to the RBF Neural Network (RBFNN) that has approaching any complicated nonlinear function relations and strong pattern recognition ability traits, the paper presents a dampening of wheat DMC system on the basic of RBFNN. The nonlinear model that has been identified by RBFNN is as the predictive model in the paper. Finally, the simulation results show that this method has good dynamic characteristics and robustness, it can take effective control of wheat dampener.

Keywords

delays; food processing industry; food products; neurocontrollers; nonlinear control systems; predictive control; process control; radial basis function networks; shock absorbers; vibration control; RBF neural network algorithm; flour production industrial processes control; model predictive control; nonlinear function relation; nonlinear time delay feature; pattern recognition; radial basis function neural network; wheat dampener control; wheat dampening MPC system; wheat watering system; Educational institutions; Heuristic algorithms; Humidity; Neural networks; Nonlinear dynamical systems; Predictive models; Training; DMC; Predictive Model; RBF Neural Network; Time Delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2013 25th Chinese

Conference_Location

Guiyang

Print_ISBN

978-1-4673-5533-9

Type

conf

DOI

10.1109/CCDC.2013.6561860

Filename

6561860