摘要
针对一类具有状态时滞的连续系统提出一种采样迭代学习控制算法。给出并证明了算法指数收敛的充分条件 ,该条件可保证系统输出无论在采样点或非采样点上 ,都能以指数收敛速率收敛至期望输出的一个与采样周期有关的误差范围内。
A sampled-data iterative learning control algorithm is proposed for continuous time systems with state delay. A condition under which the algorithm is exponential convergence is presented. The conclusion is proven by induction. It is shown that exponential convergence is achieved not only at every sampling instant but the entire time span. A numerical example shows the effectiveness of the proposed algorithm.
出处
《控制与决策》
EI
CSCD
北大核心
2001年第6期869-872,共4页
Control and Decision
基金
国家自然科学基金项目 (6 9874 0 2 5 )
关键词
采样迭代学习控制
时滞系统
指数收敛
计算机控制
Computer control
Computer simulation
Convergence of numerical methods
Iterative methods
Learning algorithms
Sampled data control systems
Theorem proving
