Adaptive Inverse Induction Machine Control Based on Variable Learning Rate BP Algorithm

Author

Xie, Shuying ; Zhang, Chengjin ; Xiao, Xiangli

Author_Institution

Shandong Univ., Jinan

fYear

2007

fDate

18-21 Aug. 2007

Firstpage

2367

Lastpage

2372

Abstract

The adaptive inverse control technology is utilized for induction machine (IM) control. Adaptive inverse control is actually an open-loop control scheme and so in the adaptive inverse control the instability problem caused by feedback control is avoided and the better dynamic performances can also be achieved. Linear LMS technique of adaptive inverse control is extended to control the MIMO, nonlinear IM based on BP neural network. And the BP algorithm is improved by using variable learning rate. Simulation study is made to validate the effectiveness of the control scheme.

Keywords

MIMO systems; adaptive control; backpropagation; feedback; induction motors; least mean squares methods; machine control; neural nets; nonlinear control systems; stability; BP neural network; MIMO control; adaptive inverse control; adaptive inverse induction machine control; feedback control; instability problem; linear LMS technique; nonlinear induction machine; open-loop control scheme; variable learning rate BP algorithm; AC machines; Adaptive control; Control systems; Induction machines; Linear feedback control systems; Machine learning; Open loop systems; Optimal control; Programmable control; Sliding mode control; adaptive inverse control; induction machine; neural network; variable learning rate;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation and Logistics, 2007 IEEE International Conference on

Conference_Location

Jinan

Print_ISBN

978-1-4244-1531-1

Type

conf

DOI

10.1109/ICAL.2007.4338973

Filename

4338973