摘要
针对普通闭环PD型迭代学习控制算法收敛速度慢且收敛精度不高的问题,通过在闭环PD型控制算法中引入动态扩张-收缩因子(dynamic expansion compression coefficient,DECC)的方法,提高闭环PD型算法的收敛速度以及收敛精度。同时将鲁棒控制引入至算法中,进一步提高算法抑制外界干扰的能力。通过构造李雅普诺夫函数证明了在所提改进的控制律作用下的信号是有界且收敛的。最后将改进的迭代学习控制算法应用在一类具有重复运行性质的非线性系统中,证明所提算法是有效的。
It aimed at the problem of slow convergence and low precision of the general closed loop PD type iterative learning control algorithm. By introducing the dynamic expansion compression coefficient（DECC）in the closed loop PD control algorithm,the convergence speed and convergence accuracy of the closed loop PD algorithm are improved. At the same time,the robust control is introduced into the algorithm,which can further improve the ability of the algorithm to restrain the external disturbance. it based on Lyapunov function proved that the proposed signal control law is improved under bounded and convergent. Finally,the improved iterative learning control algorithm is applied to a class of nonlinear systems with repetitive operations,and it is proved that the proposed algorithm is effective.
作者
郝晓弘
周勃
HAO Xiao-hong;ZHOU Bo(College of Computer and Communication, Lanzhou University of Technology, Gansu Lanzhou 730050, China;College of Electrical and Information Engineering, Lanzhou University of Technology, Gansu Lanzhou 730050, China)
出处
《机械设计与制造》
北大核心
2018年第6期29-32,共4页
Machinery Design & Manufacture
基金
国家自然基金项目(61263008
61540033)
关键词
非线性系统
迭代学习控制
PD型学习率
动态扩张-收缩因子
Nonlinear System
I terative Learning Control
PD-Type Learning Algorithm
Dynamic Expansion Compression Coefficient
