RBF Neural Network Based Adaptive Constrained PID Control of a Solid Oxide Fuel Cell

被引：0

作者：

Xu, Dezhi ^{[1
]}

Yan, Wenxu ^{[2
]}

Ji, Nan ^{[1
]}

机构：

[1] Jiangnan Univ, Inst Automat, Key Lab Adv Proc Control Light Ind, Minist Educ, 1800 Lihu Rd, Wuxi 214122, Peoples R China

[2] Jiangnan Univ, Coll Internet Things, 1800 Lihu Rd, Wuxi 214122, Peoples R China

来源：

PROCEEDINGS OF THE 28TH CHINESE CONTROL AND DECISION CONFERENCE (2016 CCDC) | 2016年

关键词：

Neural network; adaptive PID control; constrained control; dynamic anti-windup; solid oxide fuel cell; PREDICTIVE CONTROL; MODEL;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper proposes a radial basis function (RBF) neural network based adaptive constrained PID control scheme for a solid oxide fuel cell (SOFC). First, a RBF neural network is designed for identification the dynamic model of SOFC. The Jacobian information can be obtained through the identification RBF model. Then, an on-line PID parameters tuning algorithm is designed by gradient descent method. At same time, in order to solve the input saturation problem, we design an anti-windup compensator for accommodate the reference. Finally, the simulation results on the dynamic model of SOFC are provided to demonstrate the effectiveness of the proposed constrained control approach.

引用

页码：3986 / 3991

页数：6

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