基于数据驱动的自适应最优迭代学习控制研究

doi:10.3901/JME.2021.17.207

机械工程学报 ›› 2021, Vol. 57 ›› Issue (17): 207-216.doi: 10.3901/JME.2021.17.207

扫码分享

基于数据驱动的自适应最优迭代学习控制研究

杨亮亮^1,2, 袁锐¹, 史伟民¹, 鲁文其¹

1. 浙江理工大学浙江省现代纺织装备技术重点实验室杭州 310018;
2. 杭州汇萃智能科技有限公司杭州 310000

收稿日期:2020-08-31 修回日期:2021-02-14 发布日期:2021-11-16
通讯作者: 杨亮亮(通信作者),男,1978年出生,副教授,硕士研究生导师。主要研究方向为高速高精运动控制。E-mail:yangliangliang@zstu.edu.cn
作者简介:袁锐,男,1996年出生,硕士研究生。主要研究方向为迭代学习控制。E-mail:2689323250@qq.com;史伟民,男,1965年出生,教授,博士研究生导师。主要研究方向为机电系统设计。E-mail:swm@zstu.edu.cn;鲁文其,男,1982年出生,副教授,硕士研究生导师。主要研究方向为特种永磁交流电机伺服系统及其关键控制技术。E-mail:luwenqi@zstu.edu.cn
基金资助:
浙江省自然科学基金（LY18E050016）和国家重点研发计划（2017YFB1304000）资助项目。

Research on Adaptive Optimal Iterative Learning Control Based on Data Driven

YANG Liangliang^1,2, YUAN Rui¹, SHI Weimin¹, LU Wenqi¹

1. Zhejiang Provincial Key Lab of Modern Textile Machinery & Technology, Zhejiang Sci-Tech University, Hangzhou 310018;
2. Hangzhou Huicui Intelligent Technology Co. Ltd., Hangzhou 310000

Received:2020-08-31 Revised:2021-02-14 Published:2021-11-16

摘要/Abstract

摘要： 传统最优迭代学习控制（Traditional optimal iterative learning control，TOILC）可以有效提高伺服系统的跟踪性能，但系统在运行过程中可能存在参数摄动，其参数在不断地缓慢变化，导致TOILC收敛性变差，进而会使系统的跟踪性能严重恶化。因此，针对系统时变特性，将非参数模型辨识与TOILC相结合提出一种基于数据驱动的自适应最优迭代学习控制（Data-driven adaptive optimal iterative learning control，DDAOILC）算法，在迭代过程中根据输入输出信号对系统名义模型进行辨识从而更新最优迭代学习控制器，该算法不需要事先获取精确的系统模型信息，弥补了TOILC的不足；仿真和试验结果表明，DDAOILC可以有效应对伺服系统时变特性，当系统有参数摄动时，仍具有较高的跟踪性能。

关键词: 迭代学习, 收敛性, 伺服系统, 辨识, 最优控制, 数据驱动

Abstract: The traditional optimal iterative learning control (TOILC) can effectively improve the tracking performance of the servo system, but there may be parametric perturbations during the operation of the system, whose parameters are constantly changing, resulting in poor convergence of TOILC, and thus the tracking performance of the system will be seriously deteriorated. Time-varying characteristics of the system, therefore, the nonparametric model identification combined with TOILC put forward a data driven based adaptive optimal iterative learning control (DDAOILC) algorithm, the iteration process according to the name of the input and output signal of the system model to identify the optimal iterative learning controller, so as to update the algorithm does not need to obtain accurate system model information in advance, make up for the deficiency of the TOILC; Simulation and experimental results show that DDAOILC can effectively deal with the servo system's time-varying characteristics, and it still has high tracking performance when the system has parameter perturbation.

Key words: iterative learning, convergence, servo system, identification, optimal control, data-driven

中图分类号:

杨亮亮, 袁锐, 史伟民, 鲁文其. 基于数据驱动的自适应最优迭代学习控制研究[J]. 机械工程学报, 2021, 57(17): 207-216.

YANG Liangliang, YUAN Rui, SHI Weimin, LU Wenqi. Research on Adaptive Optimal Iterative Learning Control Based on Data Driven[J]. Journal of Mechanical Engineering, 2021, 57(17): 207-216.

参考文献

[1] ARIMOTO S,KAWAMURA S,MIYAZAKI F. Bettering operation of robots by learning[J]. Robot. Syst.,1984,1(2):123-140.
[2] KIM D I,KIM S. An iterative learning control method with application for CNC machine tools[J]. IEEE Transactions on Industry Applications,1996,32(1):66-72.
[3] MOORE K L. An observation about monotonic convergence in discrete-time,P-type iterative learning control[C]//IEEE International Symposium on Intelligent Control. IEEE,2001:45-49.
[4] LIU G,HOU Z. Iterative learning based model free adaptive control for subway trains with speed and input constraints[C]//Chinese Control Conference (CCC). 2019:2436-2441.
[5] NORRLÖF M,GUNNARSSON S. Experimental comparison of some classical iterative learning control algorithms[J]. IEEE Transactions on Robotics & Automation,2002,18(4):636-641.
[6] GE X,STEIN J L,ERSAL T. Frequency-domain analysis of robust monotonic convergence of norm-optimal iterative learning control[J]. IEEE Transactions on Control Systems Technology,2018,26(2):637-651.
[7] LIU L,DAI X,ZHANG W,et al. A data-driven model of optimal iterative learning control for distributed parameter systems[C]//International Conference on Information,Cybernetics,and Computational Social Systems (ICCSS). IEEE,2018:495-498.
[8] ZHANG Xinxin,LI Min,DING Huafeng,et al. Data-driven tuning of feedforward controller structured with infinite impulse response filter via iterative learning control[J]. IET Control Theory Applications,2019,13(8):1062-1070.
[9] BRISTOW D A,THARAYIL M,ALLEYNE A G. A survey of iterative learning control[J]. IEEE Control Systems,2006,26(3):96-114.
[10] 马航,杨俊友,袁琳. 迭代学习控制研究现状与趋势[J]. 控制工程, 2009,16(3):286-290. MA Hang,YANG Junyou,YUAN Lin. Current state and trend of iterative learning control[J]. Control Engineering,2009,16(3):286-290.
[11] 杨亮亮,胡建. 基于最优控制迭代学习算法的收敛性研究[J]. 机电工程,2018,35(4):397-401. YANG Liangliang,HU Jian. Research of convergence based on optimal control iterative learning algorithm[J]. Mechanical and Electrical Engineering,2018,35(4):397-401.
[12] 杨亮亮,王杰,王飞,等. 基于最优控制迭代学习的直线伺服系统振动抑制研究[J]. 机械工程学报,2019,55(15):217-225. YANG Liangliang,WANG Jie,WANG Fei,et al. Research on vibration suppression of linear servo system based on optimal control iterative learning[J]. Journal of Mechanical Engineering,2019,55(15):217-225.
[13] GUNNARSSON S,NORRLÖF M. Some aspects of an optimization approach to iterative learning control[C]//IEEE Conference on Decision & Control. IEEE,1999:1581-1586.
[14] GUNNARSSON S,NORRLÖF M. On the design of ILC algorithms using optimization[J]. Automatica,2001,37(12):2011-2016.
[15] DIJKSTRA B G,BOSGRA O H. Extrapolation of optimal lifted system ILC solution,with application to a wafer stage[C]//American Control Conference. IEEE,2002:2595-2600.
[16] DIJKSTRA B G,BOSGRA O H. Exploiting iterative learning control for input shaping,with application to a wafer stage[C]//American Control Conference. IEEE,2003:4811-4815.
[17] van OOSTEN C L,BOSGRA O H,DIJKSTRA B G. Reducing residual vibrations through iterative learning control,with application to a wafer stage[C]//American Control Conference. IEEE,2004:5150-5155.
[18] LIN N,CHI R,HUANG B. Linear time-varying data model-based iterative learning recursive least squares identifications for repetitive systems[J]. IEEE Access,2019,7:133304-133313.
[19] ZHOU L,JIA L,WANG Y L. Quadratic-criterion-based model predictive iterative learning control for batch processes using just-in-time-learning method[J]. IEEE Access,2019,7:113335-113344.
[20] YU Q,HOU Z,XU J X. D-type ILC based dynamic modeling and norm optimal ILC for high-speed trains[J]. IEEE Transactions on Control Systems Technology,2018,26(2):652-663.
[21] CHI R,HOU Z,HUANG B,et al. A unified data-driven design framework of optimality-based generalized iterative learning control[J]. Computers & Chemical Engineering,2015,77(9):10-23.
[22] CHI R,HOU Z,JIN S,et al. Improved data-driven optimal TILC using time-varying input signals[J]. Journal of Process Control,2014,24(12):78-85.

基于数据驱动的自适应最优迭代学习控制研究

Research on Adaptive Optimal Iterative Learning Control Based on Data Driven

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李朝峰, 乔瑞环, 苗雪阳, 江雨霖. 螺栓连接界面非线性建模及其参数辨识方法研究[J]. 机械工程学报, 2021, 57(7): 78-86.
[2]	李文龙, 谢核, 尹周平, 丁汉. 机器人加工几何误差建模研究：I空间运动链与误差传递[J]. 机械工程学报, 2021, 57(7): 154-168.
[3]	李文龙, 谢核, 尹周平, 丁汉. 机器人加工几何误差建模研究：II参数辨识与位姿优化[J]. 机械工程学报, 2021, 57(7): 169-184.
[4]	文科, 张加波, 乐毅, 周莹皓, 杨继之, 陈钦韬. 数控驱动的移动铣削机器人精度提升方法[J]. 机械工程学报, 2021, 57(5): 72-80.
[5]	张俊, 蒋舒佳, 池长城. 2UPR&2RPS型冗余驱动并联机器人的运动学标定[J]. 机械工程学报, 2021, 57(15): 62-70.
[6]	周子游, 刘永刚, 杨阳, 陈峥, 舒星, 秦大同. 考虑混杂充电数据的锂离子电池容量估计[J]. 机械工程学报, 2021, 57(14): 1-9.
[7]	匡柯, 孙跃东, 任东生, 韩雪冰, 郑岳久, 耿兆杰. 车用锂离子电池电化学-热耦合高效建模方法[J]. 机械工程学报, 2021, 57(14): 10-22.
[8]	蔡雪, 张彩萍, 张琳静, 张维戈, 高乐. 基于等效电路模型的锂离子电池峰值功率估计的对比研究[J]. 机械工程学报, 2021, 57(14): 64-76.
[9]	陈鸿燕, 丁虎, 陈立群. 车路耦合振动中Galerkin截断收敛阶数的研究[J]. 机械工程学报, 2021, 57(12): 31-39.
[10]	李沐蓉, 雷勇, 黄成, 胡英达, 杜世伦, 关昊天, 高德东. 基于迭代学习算法的柔性针轨迹规划[J]. 机械工程学报, 2021, 57(11): 128-137.
[11]	郑怀亮, 王日新, 杨远涛, 尹建程, 徐敏强. 数据驱动故障诊断方法泛化性能的经验性分析[J]. 机械工程学报, 2020, 56(9): 102-117.
[12]	钱立军, 吴冰, 仇多洋, 胡伟龙. 基于hp自适应伪谱法的自主泊车路径规划[J]. 机械工程学报, 2020, 56(4): 125-134.
[13]	穆永保,岳国良,闫柯柯,王璐,吴伟丽,奚涛,曹君芳. 基于ADPSS和Sobol的弧光接地暂态特征分析与影响因素关键性辨识^*[J]. 电气工程学报, 2020, 15(3): 97-104.
[14]	陈鼎, 侯亮, 祝青园, 卜祥建. 考虑轮胎几何与胎压因素的轮地相互作用力模型及其参数辨识[J]. 机械工程学报, 2020, 56(2): 174-183.
[15]	王波, 郝云晓, 权龙, 王翔宇, 葛磊, 赵斌. 分腔独立变转速泵控电液伺服系统特性研究[J]. 机械工程学报, 2020, 56(18): 235-243.