A Data-Driven Adaptive Iterative Learning Predictive Control for a Class of Discrete-time Nonlinear Systems

被引：0
|
作者：
Sun Heqing [1 ]
Hou Zhongsheng [1 ]
机构：
[1] Beijing Jiaotong Univ, Adv Control Syst Lab, Sch Elect & Informat Engn, Beijing 100044, Peoples R China
来源：
PROCEEDINGS OF THE 29TH CHINESE CONTROL CONFERENCE | 2010年
关键词：
Data-driven Control; Iterative Learning Control; Predictive Control; Model Free Adaptive Control;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
On the basis of dynamic linearization method along the iteration axis, a novel data-driven adaptive iterative learning predictive control (AILPC) is presented for a class of general repeatable discrete-time nonlinear systems. The highlight of the algorithm is that the controller design only depends on the I/O data of the dynamical system without using any priori knowledge of the system. The monotonic convergence and effectiveness of the AILPC algorithm are proven and verified through rigorous analyses, numerical example and freeway traffic flow control application.
引用
收藏
页码：5871 / 5876
页数：6
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