Affiliation:
1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
Abstract
In order to solve the problem of poor control performance, caused by fixed parameters of the active disturbance rejection control (ADRC) in bearingless permanent magnet synchronous motors (BPMSM), a dynamic parameters adjustment method of ADRC, based on a genetic algorithm and back-propagation neural network (GA-BPNN), is proposed. Firstly, the ADRC control models of motor-side and suspension-side are established, according to the motor speed formula and suspension force formula. Secondly, the BPNN algorithm is used to dynamically adjust the parameters of the ADRC, and the operation processes of BPNN are deduced, according to the chain rule. Thirdly, in order to avoid the problem of getting out of control, caused by the convergence failure of BPNN, a GA based on floating point coding is used to optimize the initial value of BPNN. Finally, these methods are integrated to form a BPMSM control system, based on the GA-BPNN-ADRC, and the effectiveness is verified on an experimental platform. The experimental results, show that the proposed method reduces the failure probability of the system from 35.61% to 0%, and the anti-interference ability and dynamic performance of the speed and displacement of the control system are significantly improved.
Funder
Postgraduate Research and Practice Innovation Program of Jiangsu Province
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Cited by
6 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献