大型互联非线性系统的分散迭代学习控制  

DECENTRALIZED ITERATIVE LEARNING CONTROL FOR LARGE-SCALE INTERCONNECTED NONLINEAR SYSTEMS

作　　者：傅勤[1] 

机构地区：[1]苏州科技学院数理学院,苏州215009

出　　处：《系统科学与数学》2013年第11期1281-1292,共12页Journal of Systems Science and Mathematical Sciences

基　　金：国家自然科学基金(11371013)资助项目

摘　　要：研究一类大型互联非线性系统的分散迭代学习控制问题,该类大型互联非线性系统具有最一般的形式.基于P型学习律和D型学习律构建得到分散迭代学习控制律,在这种分散式控制方案中,每个子系统的控制器仅依赖于该子统的输出变量,不需要与其它子系统交换信息.利用压缩映射原理,证明这两种学习律都能使得每个子系统的输出跟踪误差沿迭代轴方向收敛.The problem of decentralized iterative learning control algorithm for a class of large-scale interconnected nonlinear systems is considered. Here, the considered large-scale interconnected nonlinear systems are assumed to be quite general. Based on P-type learning scheme and D-type learning scheme, decentralized iterative learning control laws are designed for such large-scale interconnected nonlinear systems. The proposed controller of each subsystem only relies on local output variables without any information exchange with other subsystems. By using the contraction mapping principle, it is shown that both schemes can guarantee the output tracking error for each subsystem converges along the iteration axis.

关 键 词：P型学习律 D型学习律 分散迭代学习控制 大型互联非线性系统 

分 类 号：O231.2[理学—运筹学与控制论]