摘要
针对不确定时滞系统讨论带有初始修正的PD型迭代学习控制算法，给出这类系统的输出极限轨迹，以及迭代输出收敛于该极限轨迹的较弱的充分条件，其中将初始条件放宽为某任意可达初始状态函数的可重复性．仿真结果表明这种算法中的初始修正项可以有效地抑制初始偏移的影响。
In this paper,by introducing an initial-update-action term into the PD-type learning law,a newlearning control algorithm is proposed for perfect tracking of a class of uncertain time-delay systems with biased initial state. A milder condition for the convergence of the learning control is proved. It is also demonstrated,by simulation,that the proposed algorithm yields a good performance even in the presence of a biasedinitial state.
出处
《控制理论与应用》
EI
CAS
CSCD
北大核心
1998年第6期853-858,共6页
Control Theory & Applications
基金
国家自然科学基金资助!69404004
关键词
鲁棒收敛性
迭代学习控制
时滞系统
PD型
initial condition problem
robust convergence
iterative learning control
time-delay systems
