抑制初态误差影响的自适应迭代学习控制

吕庆

doi:10.16383/j.aas.2015.c140670

抑制初态误差影响的自适应迭代学习控制

doi: 10.16383/j.aas.2015.c140670

吕庆

1.
天津师范大学计算机与信息工程学院天津 300387

详细信息

作者简介:
吕庆天津师范大学计算机与信息工程学院讲师. 2014 年于南开大学获得博士学位. 主要研究方向为迭代学习控制.E-mail: jsjlvqing@mail.tjnu.edu.cn

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- 文章访问数: 1709
- HTML全文浏览量: 83
- PDF下载量: 926
- 被引次数: 0
出版历程
- 收稿日期: 2014-09-17
- 修回日期: 2015-01-24
- 刊出日期: 2015-07-20

Adaptive Iterative Learning Control for Inhibition Effect of Initial State Random Error

LV Qing

1.
College of Computer and Information Engineerig, Tianjin Nor-mal University, Tianjin 300387

摘要

摘要: 针对一类参数化高阶不确定非线性连续系统, 设计迭代学习控制算法, 以解决随机初态对系统跟踪性能产生负面影响的问题. 结合滑模控制思想以及部分限幅参数学习律, 控制算法在预设时间段内抑制随机初态偏差对系统跟踪性能的影响. 经过预设时间后, 随着迭代次数的增加, 系统的跟踪误差及其各阶导数一致收敛到零. 且在整个运行时间段内, 系统各个变量一致有界. 此外, 本文回避了非参数化不确定非线性系统在放宽迭代初值假设时常使用的Lipschitz假设条件, 而采用类Lyapunov函数分析法设计迭代学习控制器. 理论证明和仿真结果都说明了该算法的有效性.
- 迭代学习控制 /
- 初始状态误差 /
- 参数化系统 /
- 滑模控制 /
- 自适应控制
Abstract: For a class of high order nonlinear continuous systems with parametric uncertainties, a novel iterative learning control algorithm is presented to improve the tracking performance when arbitrary initial states are taken into account. Inhibition of poor tracking performance caused by random initial state errors depends on sliding mode technique and partially-saturated parameter learning mechanism in a pre-specified time interval. As a result, tracking errors and their derivatives will converge asymptotically to zero with the increase of iterations after the above-mentioned interval. In addition, all variables are uniformly bounded in the whole operation period. Moreover, the iterative learning controller is designed in this paper based on Lyapunov-like synthesis, instead of the Lipschitz condition used in nonlinear systems with nonparametric uncertainties. Both theoretical proof and simulation results show the effectiveness of the proposed algorithm.
- Iterative learning control /
- initial state error /
- pa-rameterized system /
- sliding mode control /
- adaptive control

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参考文献(43)

[1]	Xu N, Ding Y S, Hao K R. Immunological mechanism inspired iterative learning control. Neurocomputing, 2014, 145(5): 392-401
[2]	Arimoto S, Kawamura S, Miyazaki F. Bettering operation of robots by learning. Journal of Robotic Systems, 1984, 1(2): 123-140
[3]	Xu W K, Chu B, Rogers E. Iterative learning control for robotic-assisted upper limb stroke rehabilitation in the presence of muscle fatigue. Control Engineering Practice, 2014, 10(31): 63-72
[4]	Chien C J, Tayebi A. Further results on adaptive iterative learning control of robot manipulators. Automatica, 2008, 44(3): 830-837
[5]	Hakvoort W B J, Aarts R G K M, van Dijk J, Jonker J B. Lifted system iterative learning control applied to an industrial robot. Control Engineering Practice, 2008, 16(4): 377-391
[6]	Heraliċ A, Christiansson A K, Lennartson B. Height control of laser metal-wire deposition based on iterative learning control and 3D scanning. Optics and Lasers in Engineering, 2012, 50(9): 1230-1241
[7]	Hou Z S, Xu J X, Yan J W. An iterative learning approach for density control of freeway traffic flow via ramp metering. Transportation Research Part C: Emerging Technologies, 2008, 16(1): 71-97
[8]	Hou Zhong-Sheng, Jin Shang-Tai, Zhao Ming. Iterative learning identification method for the macroscopic traffic flow model. Acta Automatica Sinica, 2008, 34(1): 64-71(侯忠生, 金尚泰, 赵明. 宏观交通流模型参数的迭代学习辨识方法. 自动化学报, 2008, 34(1): 64-71)
[9]	Zheng Y C, Zhang Y, Hu J M. Iterative learning based adaptive traffic signal control. Journal of Transportation Systems Engineering and Information Technology, 2010, 10(6): 34-40
[10]	Hou Z S, Yan J W, Xu J X, Li Z J. Modified iterative-learning-control-based ramp metering strategies for freeway traffic control with iteration-dependent factors. IEEE Transactions on Intelligent Transportation Systems, 2012, 13(2): 606-618
[11]	Wu Y, Zou Q Z, Su C M. A current cycle feedback iterative learning control approach for AFM imaging. IEEE Transactions on Nanotechnology, 2009, 8(4): 515-527
[12]	Wu Y, Zou Q Z. Iterative control approach to compensate for both the hysteresis and the dynamics effects of piezo actuators. IEEE Transactions on Control Systems Technology, 2007, 15(5): 936-944
[13]	Leang K K, Devasia S. Design of hysteresis-compensating iterative learning control for piezo-positioners: application to atomic force microscopes. Mechatronics, 2006, 16(3-4): 141-158
[14]	Yan Y, Wang H M, Zou Q Z. A decoupled inversion-based iterative control approach to multi-axis precision positioning: 3D nanopositioning example. Automatica, 2012, 48(1): 167-176
[15]	Wang Z H, Zou Q Z, Faidley L, Kim G Y. Dynamics compensation and rapid resonance identification in ultrasonic-vibration-assisted microforming system using magnetostrictive actuator. IEEE/ASME Transactions on Mechatronics, 2011, 16(3): 489-497
[16]	Lv Qing, Fang Yong-Chun, Ren Xiao. Iterative learning control for accelerated inhibition effect of initial state random error. Acta Automatica Sinica, 2014, 40(7): 1295-1302(吕庆, 方勇纯, 任逍. 加速抑制随机初态误差影响的迭代学习控制. 自动化学报, 2014, 40(7): 1295-1302)
[17]	Huang S N, Tan K K, Lee T H. Necessary and sufficient condition for convergence of iterative learning algorithm. Automatica, 2002, 38(7): 1257-1260
[18]	Li X D, Chow T W S, Ho J K L. 2-D system theory based iterative learning control for linear continuous systems with time delays. IEEE Transactions on Circuits and Systems I: Regular Papers, 2005, 52(7): 1421-1430
[19]	Jiang P, Chen H D, Bamforth L C A. A universal iterative learning stabilizer for a class of MIMO systems. Automatica, 2006, 42(6): 973-981
[20]	Bu X H, Yu F S, Hou Z S, Yang H Z. Robust iterative learning control for nonlinear systems with measurement disturbances. Journal of Systems Engineering and Electronics, 2012, 23(6): 906-913
[21]	Li X D, Chow T W S, Ho J K L. Iterative learning control for linear time-variant discrete systems based on 2-D system theory. IEE Proceedings Control Theory and Applications, 2005, 152(1): 13-18
[22]	Huang D Q, Xu J X. Steady-state iterative learning control for a class of nonlinear PDE processes. Journal of Process Control, 2011, 21(8): 1155-1163
[23]	Zhang Yu-Dong, Fang Yong-Chun. Learning control for systems with saturated output. Acta Automatica Sinica, 2011, 37(1): 92-98(张玉东, 方勇纯. 一类输出饱和系统的学习控制算法研究. 自动化学报, 2011, 37(1): 92-98)
[24]	Zhang J, Yemashov S, Pantelelis N G. Iterative learning control of a reactive polymer composite moulding process. Procedia Engineering, 2012, 42: 1100-1105
[25]	Wang L M, Mo S Y, Zhou D H, Gao F R, Chen X. Delay-range-dependent robust 2D iterative learning control for batch processes with state delay and uncertainties. Journal of Process Control, 2013, 23(5): 715-730
[26]	Sun M X, Wang D W. Iterative learning control with initial rectifying action. Automatica, 2002, 38(7): 1177-1182
[27]	Chien C J, Hus C T, Yao C Y. Fuzzy system-based adaptive iterative learning control for nonlinear plants with initial state errors. IEEE Transactions on Fuzzy Systems, 2004, 12(5): 724-732
[28]	Chi R H, Hou Z S, Xu J X. Adaptive ILC for a class of discrete-time systems with iteration-varying trajectory and random initial condition. Automatica, 2008, 44(8): 2207-2213
[29]	Li X D, Xiao T F, Zheng H X. Adaptive discrete-time iterative learning control for non-linear multiple input multiple output systems with iteration-varying initial error and reference trajectory. IET Control Theory & Applications, 2011, 5(9): 1131-1139
[30]	Chi R H, Zhang D X, Liu X M, Hou Z S, Jin S T. An anticipatory terminal iterative learning control approach with applications to constrained batch processes. Chinese Journal of Chemical Engineering, 2013, 21(3): 271-275
[31]	Zhang C L, Li J M. Adaptive iterative learning control for nonlinear pure-feedback systems with initial state error based on fuzzy approximation. Journal of the Franklin Institute, 2014, 351(3): 1483-1500
[32]	Qi Li-Qiang, Sun Ming-Xuan, Guan Hai-Wa. Finite-time iterative learning control for systems with nonparametric uncertainties. Acta Automatica Sinica, 2014, 40(7): 1320-1327(齐丽强, 孙明轩, 管海娃. 非参数不确定系统的有限时间迭代学习控制. 自动化学报, 2014, 40(7): 1320-1327)
[33]	Xu J X, Tang Y. A composite energy function-based learning control approach for nonlinear systems with time-varying parametric uncertainties. IEEE Transactions on Automatic Control, 2002, 47(11): 1940-1945
[34]	Xu J X, Yan R. On initial conditions in iterative learning control. IEEE Transactions on Automatic Control, 2005, 50(9): 1349-1354
[35]	Xu J X, Jin X. State-constrained iterative learning control for a class of MIMO systems. IEEE Transactions on Automatic Control, 2013, 58(5): 1322-1327
[36]	Sun Ming-Xuan, Yan Qiu-Zhen. Error tracking of iterative learning control systems. Acta Automatica Sinica, 2013, 39(3): 251-262(孙明轩, 严求真. 迭代学习控制系统的误差跟踪设计方法. 自动化学报, 2013, 39(3): 251-262)
[37]	Xu J X, Xu J. On iterative learning from different tracking tasks in the presence of time-varying uncertainties. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 2004, 34(1): 589-597
[38]	Chen W, Chen Y Q. Robust iterative learning control for output tracking via second-order sliding mode technique. In: Proceedings of the 2010 American Control Conference. Baltimore, MD: IEEE, 2010. 2051-2056
[39]	Yin C K, Xu J X, Hou Z S. A high-order internal model based iterative learning control scheme for nonlinear systems with time-iteration-varying parameters. IEEE Transactions on Automatic Control, 2010, 55(11): 2665-2670
[40]	Chen W, Chen Y Q, Yeh C P. Robust iterative learning control via continuous sliding-mode technique with validation on an SRV02 rotary plant. Mechatronics, 2012, 22(5): 588-593
[41]	Zhang Dong-Mei, Sun Ming-Xuan, Yu Li. Observer-based iterative learning control for non-identical trajectory tracking. Control Theory & Applications, 2006, 23(5): 795-799(张冬梅, 孙明轩, 俞立. 基于观测器跟踪非一致轨迹的迭代学习控制器设计. 控制理论与应用, 2006, 23(5): 795-799)
[42]	Xu Li. Relation of uniform continuity and uniform convergence and equicontinuity on sequence of functions. Journal of Shanghai University of Electric Power, 2007, 23(3): 284-286(徐丽. 函数列一致连续和一致收敛及等度连续的关系. 上海电力学院学报, 2007, 23(3): 284-286)
[43]	Fang Yong-Chun, Lu Gui-Zhang. Nonlinear System Theory. Beijing: Tsinghua University Press, 2009. 85-87(方勇纯, 卢桂章. 非线性系统理论. 北京: 清华大学出版社, 2009. 85-87)

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抑制初态误差影响的自适应迭代学习控制

doi: 10.16383/j.aas.2015.c140670

作者简介:
吕庆天津师范大学计算机与信息工程学院讲师. 2014 年于南开大学获得博士学位. 主要研究方向为迭代学习控制.E-mail: jsjlvqing@mail.tjnu.edu.cn

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Adaptive Iterative Learning Control for Inhibition Effect of Initial State Random Error

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抑制初态误差影响的自适应迭代学习控制

doi: 10.16383/j.aas.2015.c140670

作者简介: 吕庆天津师范大学计算机与信息工程学院讲师. 2014 年于南开大学获得博士学位. 主要研究方向为迭代学习控制.E-mail: jsjlvqing@mail.tjnu.edu.cn

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出版历程

Adaptive Iterative Learning Control for Inhibition Effect of Initial State Random Error

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出版历程

目录

作者简介:
吕庆天津师范大学计算机与信息工程学院讲师. 2014 年于南开大学获得博士学位. 主要研究方向为迭代学习控制.E-mail: jsjlvqing@mail.tjnu.edu.cn