0
Research Papers

Data-Weighting Based Discrete-Time Adaptive Iterative Learning Control for Nonsector Nonlinear Systems With Iteration-Varying Trajectory and Random Initial Condition

[+] Author and Article Information
Ronghu Chi1

School of Automation and Electronics Engineering,  Qingdao University of Science and Technology, Qingdao 266042, P. R. C. e-mail: ronghu_chi@hotmail.comAdvanced Control Systems Lab, School of Electronics and Information Engineering,  Beijing Jiaotong University, Beijing 100044, P. R. C.

Zhongsheng Hou

School of Automation and Electronics Engineering,  Qingdao University of Science and Technology, Qingdao 266042, P. R. C. e-mail: ronghu_chi@hotmail.comhouzhongsheng@china.comAdvanced Control Systems Lab, School of Electronics and Information Engineering,  Beijing Jiaotong University, Beijing 100044, P. R. C.houzhongsheng@china.com

Shangtai Jin

School of Automation and Electronics Engineering,  Qingdao University of Science and Technology, Qingdao 266042, P. R. C. e-mail: ronghu_chi@hotmail.comAdvanced Control Systems Lab, School of Electronics and Information Engineering,  Beijing Jiaotong University, Beijing 100044, P. R. C.

1

Corresponding author.

J. Dyn. Sys., Meas., Control 134(2), 021016 (Jan 12, 2012) (10 pages) doi:10.1115/1.4005272 History: Received February 10, 2009; Revised July 27, 2011; Published January 11, 2012; Online January 12, 2012

In this paper, a new discrete-time adaptive iterative learning control (AILC) approach is presented to deal with nonsector nonlinearities by incorporating a recursive least-squares algorithm with a nonlinear data weighted coefficient. This scheme is also extended as a d-iteration-ahead adaptive iterative learning predictive control to address for multiple inputs multiple outputs (MIMO) nonlinear systems with unknown input gains. A major distinct feature of the presented methods is that the global stability result is obtained through Lyapunov analysis without assuming any linear growth condition on the nonlinearities. Another distinct feature is that the pointwise convergence of the presented methods is achieved over a finite interval without requiring any identical conditions on the initial states and reference trajectory.

FIGURES IN THIS ARTICLE
<>
Copyright © 2012 by American Society of Mechanical Engineers
Your Session has timed out. Please sign back in to continue.

References

Figures

Grahic Jump Location
Figure 1

The profile of random initial state value xk(0)

Grahic Jump Location
Figure 2

The target trajectory at the first four iterations

Grahic Jump Location
Figure 3

The asymptotic convergence of the tracking error by means of the modified AILC

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In