引用本文:刘乐,宋红姣,方一鸣,等.基于ELM的永磁直线同步电机位移跟踪动态面反步滑模控制[J].控制与决策,2020,35(10):2549-2555
【打印本页】   【HTML】   【下载PDF全文】   查看/发表评论  【EndNote】   【RefMan】   【BibTex】
←前一篇|后一篇→ 过刊浏览    高级检索
本文已被:浏览次   下载 本文二维码信息
码上扫一扫!
分享到: 微信 更多
基于ELM的永磁直线同步电机位移跟踪动态面反步滑模控制
刘乐1, 宋红姣1, 方一鸣1,2, 蔡满军1
(1. 燕山大学电气工程学院,河北秦皇岛066004;2. 国家冷轧板带装备及工艺工程技术研究中心,河北秦皇岛066004)
摘要:
针对永磁直线同步电机(PMLSM)易受到参数摄动、负载扰动等不确定因素的影响,进而影响其位移跟踪控制精度的问题,提出一种基于非线性干扰观测器(NDO)和极限学习机(ELM)的动态面反步滑模控制方法.首先,通过构造NDO对系统模型中的非匹配不确定项进行动态观测,并将反步控制、动态面控制与滑模控制相结合,完成PMLSM位移跟踪控制器的设计,在提高系统抗干扰能力的同时,避免常规反步控制中的“微分爆炸”问题;其次,采用ELM神经网络对系统模型中的匹配不确定项进行逼近估计,并将输出的估计值引入设计的动态面反步滑模控制器中进行补偿;再次,采用人工鱼群-蛙跳混合算法对所设计控制器的主要参数进行优化设计,提高系统的收敛速度和稳定精度;最后,将所提出控制方法与其他控制方法进行仿真对比,仿真结果表明了所提出方法的有效性.
关键词:  永磁直线同步电机  非线性干扰观测器  动态面反步滑模控制  极限学习机  人工鱼群算法  蛙跳算法
DOI:10.13195/j.kzyjc.2019.0133
分类号:TP273
基金项目:国家自然科学基金项目(61803327,61873226);河北省自然科学基金项目(F2016203263);河北省高等学校科学技术研究项目(Z2017041);河北省重点研发计划项目(18212109);燕山大学基础研究专项课题(16LGA005).
Dynamic surface backstepping sliding mode control for the displacement tracking of permanent magnet linear synchronous motor based on extreme learning machine
LIU Le1,SONG Hong-jiao1,FANG Yi-ming1,2,CAI Man-jun1
(1. College of Electrical Engineering,Yanshan University,Qinhuangdao 066004,China;2. National Engineering Research Center for Equipment and Technology of Cold Strip Rolling,Qinhuangdao 066004,China)
Abstract:
For the problem of that the permanent magnet linear synchronous motor(PMLSM) is prone to be affected by uncertain factors such as parameter perturbation, load disturbance, etc., which affects its displacement tracking control accuracy, a dynamic surface backstepping sliding mode control method is proposed based on the nonlinear disturbance observer(NDO) and the extreme learning machine(ELM). Firstly, the NDO is developed to dynamically observe the mismatched uncertainty in the system model, and the displacement tracking controllers for the PMLSM are presented by combining backstepping control with dynamic surface control and sliding mode control, which improves the anti-interference ability of the system, and avoids the ``differential explosion'' problem during using the conventional backstepping control. Then, ELM neural networks are used to approximate the matched uncertainties in the system model, and the estimated values of the outputs are introduced into the designed dynamic surface backstepping sliding mode controllers for compensation. Furthermore, the artificial fish-frog jump hybrid algorithm is adopted to optimize the main parameters of the designed controllers, which improves the convergence speed and stability accuracy of the system. Finally, the proposed control method is compared with other control methods, and the simulation results verify the effectiveness of the proposed control method.
Key words:  permanent magnet linear synchronous motor  nonlinear disturbance observer  dynamic surface backstepping sliding mode control  extreme learning machine  artificial fish swarm algorithm  frog jump algorithm

用微信扫一扫

用微信扫一扫