摘要
针对迭代学习控制算法在线性离散系统中的收敛性问题,建立了直线电机的离散化数学模型,并将迭代学习控制算法运用到电机控制系统,对其稳定性和收敛性进行了研究。提出了一种基于最优控制理论的迭代学习控制算法,利用迭代学习控制的收敛条件对所提出的控制算法的稳定性和收敛性条件进行了分析,并在前馈及反馈二自由度控制结构的基础上进行了控制器的设计;同时通过对前馈控制力引入一个加权矩阵系数,提高了基于最优控制理论的迭代学习控制算法的收敛速度,将其运用到Matlab仿真平台和实际机电控制系统。研究结果表明:基于最优控制理论和加权矩阵系数的迭代学习控制算法收敛效果显著,提高了运动轨迹跟踪性能。
Aiming at the convergence problem of iterative learning control in linear discrete systems,the discrete mathematical model of the linear motor was established,and the iterative learning control algorithm was applied to the motor control system,whose stability and convergence were studied.An iterative learning control algorithm based on optimal control theory was proposed,the stability and convergence conditions of which were analyzed by the convergence condition of iterative learning control.The controller was designed based on a typical two-degree-of-freedom controller structure with feedforward-feedback control strategy.At the same time,a weighting matrix coefficient on feedforward control force was introduced to improve the convergence speed of iterative learning algorithm,which was applied to the Matlab simulation platform and the actual electromechanical control system.The results show that the iterative learning control algorithm based on optimal control theory and weighting matrix coefficient has a significant convergence effect and improves the trajectory tracking performance.
作者
杨亮亮
胡建
YANG Liang-liang;HU Jian(Faculty of Mechanical and Automatic Control,Zhejiang Sci-Tech University,Hangzhou 310018,China)
出处
《机电工程》
CAS
北大核心
2018年第4期397-401,共5页
Journal of Mechanical & Electrical Engineering
基金
国家自然科学基金资助项目(51305404)
浙江省自然科学基金资助项目(LY18E050016)
关键词
迭代学习控制
最优控制
收敛性
加权矩阵系数
iterative learning control(ILC)
optimal control
convergence
weighted matrix coefficient
作者简介
杨亮亮(1978-),男,湖北荆门人,副教授,硕士生导师,主要从事高速高精运动控制方面的研究。E-mail:yangliangliang@zstu.edu.cn
