Please wait a minute...
浙江大学学报（理学版）
浙江大学学报（理学版）  2019, Vol. 46 Issue (3): 328-332    DOI: 10.3785/j.issn.1008-9497.2019.03.010
数学与计算机科学     
任意初态下的扰动系统学习控制
李国军1, 周国民2, 陈东杰1, 许中石1
1.浙江警察学院 公共基础部，浙江 杭州 310053
2.浙江警察学院 计算机与信息技术系，浙江 杭州 310053
Learning control for the disturbance system with arbitrary initial state error
Guojun LI1, Guomin ZHOU2, Dongjie CHEN1, Zhongshi XU1
1.Basic Courses Department, Zhejiang Police College, Hangzhou 310053, China
2.Department of Computer and Information Technology, Zhejiang Police College, Hangzhou 310053, China
 全文: PDF(523 KB)   HTML  
摘要： 针对任意初态下带有扰动的线性定常系统, 提出了相应的控制算法。该算法将受控过程分成无穷个子过程, 系统利用上一子过程的输入输出信息来调节当前过程的输入，以期获得更好的控制效果。在控制过程中, 基于迭代学习控制思想, 借助初始修正手段, 可使系统的跟踪误差达到任意小, 且当系统无扰动时，在指定区间内可实现完全跟踪。最后, 通过仿真算例, 验证了算法的有效性。
关键词： 学习控制;  初始修正;  收敛    
Abstract: A control algorithm for the linear time-invariant system is presented in this paper. It divides the controlled process into infinite subprocesses. For each subprocess, the system uses the input and output information of the last subprocess to adjust the current input in order to achieve a better control effect. In the control process, based on the iterative learning control theory, this algorithm solves the control problem using the initial rectifying methods. It can make the tracking error of the system converge to an arbitrarily small range, and achieve complete tracking if there is no disturbance in the specified time period. Finally, the effectiveness of the stated algorithm is demonstrated by the simulation result.
Key words: learning control    initial rectifying    convergence
收稿日期: 2018-07-02 出版日期: 2019-05-25
CLC:  O231.2  
基金资助: 浙江省自然科学基金资助项目(LQ18G010001)；浙江警察学院校级课题(2017QTY042).
作者简介: 李国军(1979—), ORCID:https://orcid.org/0000-0002-1338-6331，男, 博士研究生, 讲师, 主要从事学习控制研究，E-mail：liguojun@zjjcxy.cn.
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章　　
李国军
周国民
陈东杰
许中石

引用本文:

李国军, 周国民, 陈东杰, 许中石. 任意初态下的扰动系统学习控制[J]. 浙江大学学报（理学版）, 2019, 46(3): 328-332.

Guojun LI, Guomin ZHOU, Dongjie CHEN, Zhongshi XU. Learning control for the disturbance system with arbitrary initial state error. Journal of ZheJIang University(Science Edition), 2019, 46(3): 328-332.

链接本文:

https://www.zjujournals.com/sci/CN/10.3785/j.issn.1008-9497.2019.03.010    或    https://www.zjujournals.com/sci/CN/Y2019/V46/I3/328

1 XUJ X, HOUZ S. On learning control: The state of the art and perspective[J]. Acta Automatica Sinica, 2005, 31(6):943-952.
2 ARIMOTOS, KAWAMURAS, MIYAZAKIF. Bettering operation of robots by learning[J]. Journal of Intelligent & Robotic Systems, 1984, 1(2):123-140.doi:10.1002/rob.4620010203
3 BRISTOWD, THARAYILM, ALLEYNEA G. A survey of iterative learning control[J]. IEEE Control Systems Magazine, 2006, 26(3):96-114.
4 ISHIHARAT, ABE K, TAKEDAH. A discrete-time design of robust iterative learning controllers[J]. IEEE Transactions on Systems Man and Cybernetics Part B, 1992, 22(1):74-84.doi:10.1109/21.141312
5 SAABS S, VOGTW G, MICKLEM H. Learning control algorithms for tracking ‘slowly’ varying trajectories[J]. IEEE Transactions on Systems Man and Cybernetics Part B, 1997, 27(4):657-670.doi:10.1109/3477.604109
6 HEINZINGERG, FENWICKD, PADENB, et al. Stability of learning control with disturbances and uncertain initial conditions[J]. IEEE Transactions on Automatic Control, 1992, 37(1):110-114.doi:10.1109/9.109644
7 WANGD W. Convergence and robustness of discrete time nonlinear systems with iterative learning control[J]. Automatica, 1998, 34(11):1445-1448.
8 ARIMOTOS. Learning control theory for robotic motion[J]. International Journal of Adaptive Control and Signal Processing, 1990, 4(6): 543-564.doi:10.1002/acs.4480040610
9 LEE H S, BIENZ. Study on robustness of iterative learning control with nonzero initial error[J]. International Journal of Control, 1996, 64(3):345-359.
10 PARKP, BIENZ, HWANGD. A study on the robustness of a PID-type iterative learning controller against initial state error[J]. International Journal of Systems Science, 1999, 30(1):49-59.
11 SUNM X, WANGD W. Initial condition issues on iterative learning control for nonlinear systems with time delay[J]. International Journal of Systems Science, 2001,32(11):1365-1375.
12 LIG J, CHEND J, HANY S. Consensus tracking of high-order multi-agent systems with initial state errors[J]. Acta Mathematicae Applocatae Sinica, 2018, 41(2): 156-171.
13 LIX D, CHOWT W S, HO J K L, et al. Iterative learning control with initial rectifying action for nonlinear continuous systems[J]. IET Control Theory & Applications, 2009, 3(1):49-55.doi:10.1049/iet-cta:20070486
14 LIX D, WEIY S. Iterative learning control for linear discrete-time systems with high relative degree under initial state vibration[J]. IET Control Theory & Applications, 2016, 10(10): 1115-1126.doi:10.1049/iet-cta.2015.0826
15 MENGD Y, MOOREK L. Robust iterative learning control for nonrepetitive uncertain systems[J]. IEEE Transactions on Automatic Control, 2017, 62(2): 907-913.doi:10.1109/tac.2016.2560961
16 XUJ X, TANY. A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties[J]. IEEE Transactions on Automatic Control, 2002, 47(11): 1940-1945.doi:10.1109/tac.2002.804460
17 TAYEBIA, CHIENC. A unified adaptive iterative learning control framework for uncertain nonlinear systems[J]. IEEE Transactions on Automatic Control, 2007, 52(10):1907-1913.
18 CHIENC , HSU C, YAOC. Fuzzy system-based adaptive iterative learning control for nonlinear plants with initial state errors[J]. IEEE Transactions on Fuzzy Systems, 2004, 12(5): 724-732.doi:10.1109/tfuzz.2004.834806
19 SUNM X. Finite-time iterative learning control[J]. Journal of Systems Science and Mathematical Sciences, 2010, 30(6): 733-741.
20 XIEH Y, SUNM X. Design of iterative learning controllers with finite-time dead-zone modification[J]. Control Theory & Applications, 2009, 26(11):1225-1231.
21 QIL Q, SUNM X, GUANH W. Finite-time iterative learning control for systems with nonparametric uncertainties[J]. Acta Automatica Sinica, 2014, 40(7):1320-1327.
22 YINC K, XUJ X, HOUZ S. A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters[J]. IEEE Transactions on Automatic Control, 2010, 56(11): 2665-2670.
[1] 金红燕,孙根年,张兴泰. 中国旅游业高质量发展水平的区域差异及收敛性[J]. 浙江大学学报（理学版）, 2023, 50(4): 495-507.
[2] 杨欣雨,陆艺,石攀岩,周丽珍. 数列和可测函数的几乎收敛性[J]. 浙江大学学报（理学版）, 2023, 50(2): 131-136.
[3] 康宁,荆科. 一类重心权Hermite有理插值的二阶导数收敛性[J]. 浙江大学学报（理学版）, 2022, 49(3): 324-328.
[4] 石金诚,肖胜中. 多孔介质中相互作用的Brinkman方程组与Darcy方程组解的收敛性[J]. 浙江大学学报（理学版）, 2022, 49(2): 151-158.
[5] 杨延涛,陈晶晶,周海云. 涉及拟反向强单调算子零点的一个弱收敛结果及其应用[J]. 浙江大学学报（理学版）, 2022, 49(1): 49-52.
[6] 李娟. 高阶对流Cahn-Hilliard型方程的二阶线性化差分方法[J]. 浙江大学学报（理学版）, 2022, 49(1): 60-65.
[7] 任敏. 随机环境中受传染性疾病影响的分枝过程的极限性质[J]. 浙江大学学报（理学版）, 2022, 49(1): 53-59.
[8] 章茜, 蔡光辉. WOD随机变量序列加权和的完全收敛性[J]. 浙江大学学报（理学版）, 2021, 48(4): 435-439.
[9] 祝平, 陈小雕, 马维银, 姜霓裳. 光滑函数实根计算的渐进显式公式[J]. 浙江大学学报（理学版）, 2021, 48(2): 143-150.
[10] 宋明珠, 邵静, 刘彩云. END随机变量序列移动平均过程的极限性质[J]. 浙江大学学报（理学版）, 2020, 47(5): 559-563.
[11] 高云峰, 邹广玉. NSD序列生成的移动平均过程的矩完全收敛性[J]. 浙江大学学报（理学版）, 2020, 47(2): 172-177.
[12] 赵喆, 张天野, 黄彦浩, 郑文庭, 陈为. 面向仿真数据的电网运行方式可视分析[J]. 浙江大学学报（理学版）, 2020, 47(1): 36-44.
[13] 章茜, 蔡光辉, 郑钰滟. WOD随机变量序列的完全收敛性[J]. 浙江大学学报（理学版）, 2019, 46(4): 412-415.
[14] 章茜. 行为两两NQD随机变量阵列加权和的完全收敛性[J]. 浙江大学学报（理学版）, 2017, 44(5): 538-541.
[15] 张理涛, 谷同祥, 孟慧丽. 解非对称鞍点问题的广义交替分裂预处理子的一个注记[J]. 浙江大学学报（理学版）, 2017, 44(2): 168-173.