| 离散时间非线性系统的数据驱动无模型自适应迭代学习控制 |
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| 引用本文： | 金尚泰,侯忠生,池荣虎,柳向斌.离散时间非线性系统的数据驱动无模型自适应迭代学习控制[J].控制理论与应用,2012,29(8):1001-1009. |
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| 作者姓名： | 金尚泰 侯忠生 池荣虎 柳向斌 |
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| 作者单位： | 1. 北京交通大学电子信息工程学院先进控制系统研究所,北京,100044
2. 青岛科技大学自动化与电气工程学院,山东青岛,266042 |
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| 基金项目： | This work is supported by National Natural Science Foundation of China (Nos. 60834001, 60974040, 61120106009), and the Fundamental Research Funds for the Central Universities (No. 2011JBM201). |
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| 摘 要： | 本文基于迭代域的动态线性化方法,提出了一类单入单出离散时间非线性系统的数据驱动无模型自适应迭代学习控制方案.无模型自适应迭代学习控制本质上属于一种数据驱动控制方法,仅利用被控对象的输入输出数据即可实现控制方案的设计.理论分析表明无模型自适应迭代学习控制方案可以保证最大学习误差的单调收敛性.数值仿真和快速路交通控制应用验证了无模型自适应迭代学习控制方案的有效性.
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| 关 键 词： | 数据驱动控制 迭代学习控制 无模型自适应控制 动态线性化方法 单调收敛性 快速路交通控制 |
| 收稿时间： | 5/2/2012 12:00:00 AM |
| 修稿时间： | 2012/6/29 0:00:00 |
Data-driven model-free adaptive iterative learning control for a class of discrete-time nonlinear systems |
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| JIN Shang-tai,HOU Zhong-sheng,CHI Rong-hu and LIU Xiang-bin.Data-driven model-free adaptive iterative learning control for a class of discrete-time nonlinear systems[J].Control Theory & Applications,2012,29(8):1001-1009. |
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| Authors: | JIN Shang-tai HOU Zhong-sheng CHI Rong-hu and LIU Xiang-bin |
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| Affiliation: | Advanced Control Systems Laboratory, School of Electronic and Information Engineering, Beijing Jiaotong University,Advanced Control Systems Laboratory, School of Electronic and Information Engineering, Beijing Jiaotong University,School of Automation and Electrical Engineering, Qingdao University of Science and Technology,Advanced Control Systems Laboratory, School of Electronic and Information Engineering, Beijing Jiaotong University |
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| Abstract: | In this paper, a data-driven model-free adaptive iterative learning control (MFAILC) scheme is proposed based on a novel dynamic linearization approach along the iteration axis for a class of repetitive discrete-time single input single output (SISO) nonlinear systems. The MFAILC is essentially a data-driven control method that designs controller merely using the measured input and output data of the controlled plant. Theoretical analysis shows that the MFAILC guarantees the monotonic convergence of the iteration maximum error. Numerical example and freeway traffic control application are given to illustrate the effectiveness of the MFAILC. |
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| Keywords: | data-driven control iterative learning control model-free adaptive control dynamic linearization approach monotonic convergence freeway traffic control |
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