The research of PMSM RBF neural network PID parameters self-tuning in elevator

Author

Wang Tong-xu ; Ma Hong-yan

Author_Institution

Sch. of Electr. & Inf. Eng., Beijing Univ. of Civil Eng. & Archit., Beijing, China

fYear

2015

fDate

23-25 May 2015

Firstpage

3350

Lastpage

3354

Abstract

To make the elevator operates more smoothly and obtain quicker response speed, the traditional PID algorithm of elevator traction machine speed system needs to be improved. This paper adopts RBF neural network PID control algorithm to control the speed system of PMSM. The simulation results verify that the dynamic performances of PMSM have been improved. In addition, combined with the elevator speed curve, the simulation results verify that the RBF neural network PID control algorithm applied to the elevator with PMSM control system is feasible.

Keywords

lifts; machine control; neurocontrollers; permanent magnet motors; radial basis function networks; self-adjusting systems; synchronous motors; three-term control; traction; velocity control; PID control algorithm; PID parameters self-tuning; PMSM control system; RBF neural network; dynamic performances; elevator speed curve; elevator traction machine speed system; permanent magnet synchronous motor; radial basis function; speed system control; Elevators; Heuristic algorithms; Mathematical model; Neural networks; PD control; Stators; Elevator; PMSM; RBF neural network;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2015 27th Chinese

Conference_Location

Qingdao

Print_ISBN

978-1-4799-7016-2

Type

conf

DOI

10.1109/CCDC.2015.7162499

Filename

7162499