DocumentCode :
3219491
Title :
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
Link To Document :
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