输出干扰对基于逆模型的迭代学习控制的影响

doi:10.3969/j.issn.0253-2778.2014.01.010

中国科学技术大学学报 ›› 2014, Vol. 44 ›› Issue (1): 79-86.DOI: 10.3969/j.issn.0253-2778.2014.01.010

• 论著 • 上一篇

输出干扰对基于逆模型的迭代学习控制的影响

刘召杰

国家电网上海市电力公司电力科学研究院，上海 200473

收稿日期:2013-09-29 修回日期:2013-12-27 接受日期:2013-12-27 出版日期:2013-12-27 发布日期:2013-12-27

Effect of output noise in inverse-model-based iterative learning control

LIU Shaojie

Electric Power Research Institute, SMEPC, Shanghai 200437, China

Received:2013-09-29 Revised:2013-12-27 Accepted:2013-12-27 Online:2013-12-27 Published:2013-12-27
About author:LIU Shaojie, male, born in 1980, PhD. Research field: Automation of electricpower systems.

摘要/Abstract

摘要： 针对具有输出干扰的线性时不变单输入单输出系统，首次运用基于逆模型的迭代学习控制理论分析其轨迹跟踪控制问题.通过严格的数学分析，提出了无干扰输出误差的数学期望计算公式（欧几里得范数）并证明了其收敛性. 进而给出了其频域分析公式，证明了系统参数、噪声光谱和基于逆模型的迭代学习控制参数对收敛性的影响.系统仿真实验结果验证了以上的理论发现.

关键词: 基于逆模型的迭代学习控制, 输出干扰, 方差, 数学期望, 欧几里得范数, 频域分析

Abstract: Inverse-model-based iterative learning control (ILC) for linear-time invariant, single-input single output (SISO) systems subject to output noise is proposed with the intent of predicting expectation of the underlying “noise-free” mean square error (Euclidean norm) on each iteration. Frequency domain formulae are derived to provide an insight into links between plant characteristics, noise spectra and inverse-model-based ILC parameters. Simulations are used to illustrate the theoretical findings.

Key words: inverse-model-based iterative learning control, output noise, variance, expectation, Euclidean norm, frequency domain

中图分类号:

刘召杰. 输出干扰对基于逆模型的迭代学习控制的影响[J]. 中国科学技术大学学报, 2014, 44(1): 79-86.

LIU Shaojie. Effect of output noise in inverse-model-based iterative learning control[J]. Journal of University of Science and Technology of China, 2014, 44(1): 79-86.

参考文献

［1］ Arimoto S, Kawamura S, Miyazaki F. Bettering operations of robots by learning［J］. Journal of Robotic Systems, 1984, 1(2): 123-140.
［2］ Moore K L. Iterative learning control for deterministic systems ［M］. Berlin, Germany: Springer-Verlag, 1992.
［3］ Bien Z, Xu J X. Iterative Learning Control: Analysis, Design, Integration and Applications ［M］. Boston, USA: Kluwer, 1998.
［4］ Chen Y Q, Wen C Y. Iterative Learning Control: Convergence, Robustness and Applications ［M］. London, UK: Springer-Verlag, 1999.
［5］ Ahn H S, Moore K L, Chen Y Q. Iterative learning control: robustness and monotonic convergence for interval systems ［M］. London: Springer-Verlag, 2007.
［6］ Bristow D A, Tharayil M, Alleyne A G. A survey of iterative learning control ［J］. IEEE Control Systems Magazine, 2006, 26(3): 96-114.
［7］ Ahn H, S Chen Y Q, Moore K L. Iterative learning control: brief survey and categorization ［J］. IEEE Transactions on Systems, Man and Cybernetics: Part C, 2007, 37(6): 1 109-1 121.
［8］ Amann N, Owens D H, Rogers E. Iterative learning control using optimal feedback and feedforward actions ［J］. International Journal of Control, 1996, 65(2): 277-293.
［9］ Owens D H, Freeman C T, van Dinh T. Norm-optimal iterative learning control with intermediate point weighting: Theory, algorithms, and experimental evaluation ［J］. IEEE Transactions on Control Systems Technology, 2013, 21(3): 999-1 007.
［10］ Owens D H, Htnen J. Iterative learning control — An optimization paradigm ［J］. Annual Reviews in Control, 2005, 29(1): 57-70.
［11］ Owens D H, Htnen J, Daley S. Robust monotone gradient-based discrete-time iterative learning control ［J］. International Journal of Robust and Non-linear Control, 2008, 19(6): 634-661.
［12］ Owens D H, Feng K. Parameter optimization in iterative learning control ［J］. International Journal of Control, 2003, 76(11): 1 059-1 069.
［13］ Owens D H, Freeman C T, Chu B. Multivariable norm optimal iterative learning control with auxiliary optimisation ［J］. International Journal of Control, 2013, 86(6): 1 026-1 045.
［14］ Owens D H, Liu S. Iterative learning control: Quantifying the effect of output noise ［J］. Control Theory and Applications, 2011, 5(2): 379-388.
［15］ Daley S, Owens D H, Htnen J. Application of optimal iterative learning control to the dynamic testing of structures ［J］. Special Issue of the Proceedings of the Institution of Mechanical Engineers, Part I, 2007, 221(1): 211-222.
［16］ Chen W, Chen Y Q, Yeh C P. Robust iterative learning control via continuous sliding-mode technique with validation on an SRV02 rotary plant ［J］. Mechatronics, 2012, 22(5): 588-593.
［17］ Bouakrif F, Boukhetala D, Boudjema F. Velocity observer-based iterative learning control for robot manipulators ［J］. International Journal of Systems Science, 2013, 44(2): 214-222.
［18］ Butcher M, Kamiri A, Longchamp R. A statistical analysis of certain iterative learning control algorithms ［J］. International Journal of Control, 2008, 81(1): 156-166.
［19］ Harte T J, Htnen J, Owens D H. Discrete-time inverse model-based iterative learning control: Stability, monotonicity and robustness ［J］. International Journal of Control, 2005, 78(8): 577-586.



[1]	房炜杰，金百锁. 方差变点的加权累积和估计的极限性质[J]. 中国科学技术大学学报, 2020, 50(4): 389-395.
[2]	邓辛凝，毕秀春，张曙光. 基于状态相依风险厌恶的最优动态协整配对交易策略[J]. 中国科学技术大学学报, 2019, 49(8): 655-667.
[3]	郝静，张鹏. 具有基数约束的多阶段均值-方差投资组合优化[J]. 中国科学技术大学学报, 2016, 46(2): 156-164.
[4]	赵亚娟. 专家群评价结果可信度分析与检验[J]. 中国科学技术大学学报, 2016, 46(2): 165-172.
[5]	杨涛，方帅，程文娟. 基于熵的图像噪声方差估计[J]. 中国科学技术大学学报, 2015, 45(4): 337-344.
[6]	杨奉豪，冯珉. 正态总体误差方差的经验Bayes估计及其优良性[J]. 中国科学技术大学学报, 2013, 43(2): 162-168.

输出干扰对基于逆模型的迭代学习控制的影响

Effect of output noise in inverse-model-based iterative learning control

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价