电工技术学报2018，Vol.33Issue(10)：2392-2399,8.DOI:10.19595/j.cnki.1000-6753.tces.171239
基于自适应修正拉盖尔递归神经网络的永磁直线同步电机反推控制
Backstepping Control Based on Adaptive Modified Laguerre ecurrent Neural Network for Permanent Magnet Linear Synchronous Motor
摘要
Abstract
A backstepping control approach based on adaptive modified Laguerre recurrent neural network (AMLRNN) was proposed for permanent magnet linear synchronous motor (PMLSM) servo system which is vulnerable to influence of the uncertainties, such as parameter variations and nonlinear external disturbances. Firstly, the dynamic model of PMLSM with the uncertainties was established. And then, two optimal learning rates were derived by the on-line parameter training methodology based on the Lyapunov stability theorem to accelerate parameter convergence. This method can avoid the inherent problem of explosion of complexity and chattering phenomenon existed in the general adaptive backstepping control system, and make the system have good transient performance and robust performance. Finally, the experimental results confirm that the proposed scheme is effective and feasible. Compared with the general adaptive backstepping control system, the backstepping control system using AMLRNN has more superior control performance, and the position tracking error of system is obviously reduced.关键词
永磁直线同步电机/拉盖尔递归神经网络/反推控制/李雅普诺夫稳定性/跟踪误差Key words
Permanent magnet linear synchronous motor/Laguerre recurrent neural network/backstepping control/Lyapunov stability/tracking error分类
信息技术与安全科学引用本文复制引用
赵希梅,吴勇慷..基于自适应修正拉盖尔递归神经网络的永磁直线同步电机反推控制[J].电工技术学报,2018,33(10):2392-2399,8.基金项目
辽宁省自然科学基金计划重点项目(20170540677)和辽宁省教育厅科学技术研究项目(LQGD2017025)资助. （20170540677）
