Design of Neural Network PID Controller Based on E-FRIT and Online Learning

被引：0

作者：

Kinoshita, Kento ^{[1
]}

Ohno, Shuichi ^{[1
]}

Wakitani, Shin ^{[1
]}

机构：

[1] Hiroshima Univ, Grad Sch Engn, 1-4-1 Kagamiyama, Higashihiroshima, Hiroshima, Japan

来源：

ICAROB 2018: PROCEEDINGS OF THE 2018 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS | 2018年

关键词：

PID control; E-FRIT; neural network; data-driven; online learning;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

PID controllers have been widely used in industrial world. When a controlled object has a nonlinear characteristic, a good control result is not always obtained with fixed PID gains. To overcome the problem, a design method of a nonlinear PID controller using a neural network has been proposed. In this paper, an online learning method of the proposed controller to attenuate the effect of the system change of the controlled object has been presented.

引用

页码：334 / 337

页数：4

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