Journal of University of Chinese Academy of Sciences >
Iterative learning control for linear time-variant continuous systems with iteration-varying initial conditions and reference trajectories
Received date: 2010-04-15
Revised date: 2010-07-21
Online published: 2011-05-15
Supported by
Supported by the National Natural Science Foundation of China (60874116) and Natural Science Foundation of Hainan province (610227)
For non-strictly repetitive linear time-variant continuous systems, both the iterative initial conditions and the reference trajectories are iteration-varying within a bound. We present a kind of iterative learning controller with a rectifying action to the non-strictly repetitive tracking. The proposed controller can make the output tracking error beyond the initial time interval converge to a residual set whose size depends on the estimation error of input matrix. Especially, when the accurate input matrix is known, the output tracking error beyond the initial time interval can approach zero.
DAI Hui , LU Yi-Min . Iterative learning control for linear time-variant continuous systems with iteration-varying initial conditions and reference trajectories[J]. Journal of University of Chinese Academy of Sciences, 2011 , 28(3) : 366 -374 . DOI: 10.7523/j.issn.2095-6134.2011.3.014
[1] Li X D, Ho J K L, Chow T W S. Iterative learning control for linear time-variant discrete systems based on 2-D system theory //IEE Proc Control Theory Appl, 2005, 152(1): 13-18.
[2] Xu J X, Yan R. Fixed point theorem-based iterative learning control for LTV systems with input singularity
[J]. IEEE Trans Automat Contr, 2003, 48(3):487-492.
[3] Jiang P, Chen H, Bamforth L C A. A universal iterative learning stabilizer for a class of MIMO systems
[J]. Automatica, 2006, 42: 973-981.
[4] Fang Y, Chow T W S. 2-D analysis for iterative learning controller for discrete-time systems with variable initial conditions
[J]. IEEE Trans Circuit and Systems, Part I: Fundamental theory and applications, 2003, 50(5): 722-727.
[5] Xu J X, Yan R. On initial conditions in iterative learning control
[J]. IEEE Trans Automat Contr, 2005, 50(9):1349-1354.
[6] Wang D. Convergence and robustness of discrete time nonlinear systems with iterative learning control
[J]. Automatica, 1998, 34(11):1445-1448.
[7] Chen Y, Wen C, Gong Z, et al. An iterative learning controller with initial state learning
[J]. IEEE Trans Automat Contr, 1999, 44(2): 371-376.
[8] Park K H. An average operator-based PD-type iterative learning control
[J]. IEEE Trans Automat Contr, 2005, 50(9):1349-1354.
[9] Xu J X, Zhu T. Dual-scale direct learning control of trajectory tracking for a class of nonlinear uncertain systems
[J]. IEEE Trans Automat Contr, 1999, 44(10):1884-1888.
[10] Saab S S, Vogt W G, Mickle M H. Learning control algorithms for tracking 'slowly’ varying trajectories
[J]. IEEE Trans Syst, Man, Cybern B, 1997, 27(4): 657-670.
[11] Xu J X, Xu J. On iterative learning from different tracking tasks in the presence of time-varying uncertainties
[J]. IEEE Trans Syst, Man, Cybern B, 2004, 34(1):589-597.
[12] Ruan X E, Bien Z Z, Park K H. Decentralized iterative learning control to large-scale industrial processes for nonrepetitive trajectory tracking
[J]. IEEE Trans Syst, Man, Cybern A, 2008, 381:238-252.
[13] Li J M, Li X M, Xing K Y. Hybrid adaptive iterative learning control of non-uniform trajectory tracking for nonlinear time-delay systems //Proc of the 26th Chinese Control Conference. Zhangjiajie, 2007: 515-519.
[14] Li X D, Ho J K L, Chow T W S. Iterative learning control with initial rectifying action for nonlinear continuous systems
[J]. IET Proc Control Theory and Applications, 2009, 3(1): 49-55.
/
| 〈 |
|
〉 |