摘要
非线性滑模控制的直线电机伺服系统,由于滑模控制算法涉及的参数较多,会给参数调优带来困难,从而影响控制效果。结合理论分析与仿真方法,以实验采集的样本为基础,通过反向传播神经网络对直线电机伺服系统进行建模;采用非奇异快速终端滑模控制作为控制算法,搭建直线电机运动控制仿真实验平台;利用粒子群算法,在给定范围内优化滑模控制参数;在仿真平台验证所提出参数优化策略的有效性。当指令正弦位移信号为d=40 sin(πt),参数优化前后直线电机的仿真跟随误差的最大值差分别为0.3770,0.1409 mm,且后者的波动范围明显小于前者。
For the linear motor servo system with non-linear sliding mode control,the sliding mode control algorithm involves many parameters,which makes it difficult to optimize the parameters and affects the control effect.Combined with theoretical analysis and simulation methods,based on the experimental samples,the linear motor servo system is modeled by BP neural network.The non�singular fast terminal sliding mode control is used as the control algorithm to build the simulation platform of linear motor motion control.PSO algorithm is used to optimize the parameters of sliding mode control.The effectiveness of the proposed parameter optimization strategy is verified on the simulation platform.When the command sinusoidal displacement signal is d=40sin(πt),the maximum tracking errors before and after parameter optimization is 0.3770 mm and 0.1409 mm respectively,and the fluctuation range of the latter is obviously smaller than the former.
作者
杨嘉伟
Yang Jiawei(University of Shanghai for Science and Technology,Shanghai 200093,China)
出处
《农业装备与车辆工程》
2022年第2期126-129,共4页
Agricultural Equipment & Vehicle Engineering
关键词
直线电机
滑模控制
跟随误差
参数调优
linear motor
sliding-mode control
tracking errors
parameter optimization
作者简介
杨嘉伟(1997-),男,硕士研究生,研究方向:直线电机控制。E-mail:1143410151@qq.com。
