A high-order internal model based iterative learning control scheme for discrete linear time-varying systems

被引：46
作者：
Zhou W. [1 ,2 ]
Yu M. [1 ]
Huang D.-Q. [3 ]
机构：
[1] College of Electrical Engineering, Zhejiang University, Hangzhou
[2] College of Engineering, Jiangsu Institute of Commerce, Nanjing
[3] Department of Aeronautics, Imperial College London, London
来源：
International Journal of Automation and Computing | 2015年 / 12卷 / 03期
关键词：
discrete linear time-varying systems; high-order internal model; iteration-varying desired trajectory; Iterative learning control; permanent magnet linear motors;
D O I：
10.1007/s11633-015-0886-x
中图分类号：
学科分类号：
摘要：
In this paper, an iterative learning control algorithm is proposed for discrete linear time-varying systems to track iteration-varying desired trajectories. A high-order internal model (HOIM) is utilized to describe the variation of desired trajectories in the iteration domain. In the sequel, the HOIM is incorporated into the design of learning gains. The learning convergence in the iteration axis can be guaranteed with rigorous proof. The simulation results with permanent magnet linear motors (PMLM) demonstrate that the proposed HOIM based approach yields good performance and achieves perfect tracking. © 2015, Institute of Automation, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg.
引用
收藏
页码：330 / 336
页数：6
相关论文
共 40 条
  • [1] Uchiyama M., Formation of high speed motion pattern of mechanical arm by trial, Transactions of the Society of Instrumentation and Control Engineers, 14, 6, pp. 706-712, (1978)
  • [2] Arimoto S., Naniwa T., Suzuki H., Robustness of P-type learning control with a forgetting factor for robotic motions, Proceedings of the 29th Conference on Decision and Control, pp. 2640-2645, (1990)
  • [3] Moore K.L., Dahleh M., Bhattacharyya S.P., Iterative learning control: A survey and new results, Journal of Robotic Systems, 9, 5, pp. 563-594, (1992)
  • [4] Yu M., Wang J.S., Qi D.L., Output-feedback adaptive learning control with unknown control direction, Journal of Zhejiang University (Engineering Science), 47, 8, pp. 1424-1430, (2013)
  • [5] Huang D.Q., Li X.F., Xu J.X., Xu C., He W., Iterative learning control of inhomogeneous distributed parameter systems-frequency domain design and analysis, Systems & Control Letters, 72, pp. 22-29, (2014)
  • [6] Arimoto S., Robustness of learning control for robot manipulators, Proceedings of IEEE International Conference on Robotics and Automation, pp. 1528-1533, (1990)
  • [7] 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, 5, 9, pp. 1131-1139, (2011)
  • [8] Hou Z.S., Xu J.X., Zhong H.W., Freeway traffic control using iterative learning control-based ramp metering and speed signaling, IEEE Transactions on Vehicular Technology, 56, 2, pp. 466-477, (2007)
  • [9] Huang D.Q., Xu J.X., Steady-state iterative learning control for a class of nonlinear PDE processes, Journal of Process Control, 21, 8, pp. 1155-1163, (2011)
  • [10] Huang D.Q., Xu J.X., Venkataramanan V., Huynh T.C.T., High-performance tracking of piezoelectric positioning stage using current-cycle iterative learning control with gain scheduling, IEEE Transactions on Industrial Electronics, 61, 2, pp. 1085-1098, (2014)
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