Robust optimal design and convergence properties analysis of iterative learning control approaches

نویسندگان

  • Jian-Xin Xu
  • Ying Tan
چکیده

In this paper, we address four major issues in the .eld of iterative learning control (ILC) theory and design. The .rst issue is concerned with ILC design in the presence of system interval uncertainties. Targeting at time-optimal (fastest convergence) and robustness properties concurrently, we formulate the ILC design into a min–max optimization problem and provide a systematic solution for linear-type ILC consisting of the .rst-order and higher-order ILC schemes. Inherently relating to the .rst issue, the second issue is concerned with the performance evaluation of various ILC schemes. Convergence speed is one of the most important factors in ILC. A learning performance index—Q-factor—is introduced, which provides a rigorous and quanti.ed evaluation criterion for comparing the convergence speed of various ILC schemes. We further explore a key issue: how does the system dynamics a8ect the learning performance. By associating the time weighted norm with the supreme norm, we disclose the dynamics impact in ILC, which can be assessed by global uniform bound and monotonicity in iteration domain. Finally we address a rather controversial issue in ILC: can the higher-order ILC outperform the lower-order ILC in terms of convergence speed and robustness? By applying the min–max design, which is robust and optimal, and conducting rigorous analysis, we reach the conclusion that the Q-factor of ILC sequences of lower-order ILC is lower than that of higher-order ILC in terms of the time-weighted norm. ? 2002 Elsevier Science Ltd. All rights reserved.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Perfect Tracking of Supercavitating Non-minimum Phase Vehicles Using a New Robust and Adaptive Parameter-optimal Iterative Learning Control

In this manuscript, a new method is proposed to provide a perfect tracking of the supercavitation system based on a new two-state model. The tracking of the pitch rate and angle of attack for fin and cavitator input is of the aim. The pitch rate of the supercavitation with respect to fin angle is found as a non-minimum phase behavior. This effect reduces the speed of command pitch rate. Control...

متن کامل

Incorporating Model Uncertainty in Iterative Learning Control: Convergence Analysis and Controller Design

In this paper, we study MIMO Iterative Learning Control (ILC) and its robustness against model uncertainty. Although it is argued that, so-called, norm optimal ILC controllers have some inherent robustness, not many results are available that can make quantitative statements about the allowable model uncertainty. In this paper, we derive sufficient conditions for robust convergence of the ILC a...

متن کامل

Convergence Analysis of A Parametric Robust H 2 Controller Synthesis Algorithm

This paper presents an iterative algorithm for solving the parametric robust H2 controller synthesis problem and analyzes the convergence properties of the algorithm on several examples. Iterative procedures are normally applied to a large class of robust control design problems in which the formulation naturally leads to bilinear matrix inequalities (BMIs). It is di cult to make concrete state...

متن کامل

Bilateral Teleoperation Systems Using Backtracking Search optimization Algorithm Based Iterative Learning Control

This paper deals with the application of Iterative Learning Control (ILC) to further improve the performance of teleoperation systems based on Smith predictor. The goal is to achieve robust stability and optimal transparency for these systems. The proposed control structure make the slave manipulator follow the master in spite of uncertainties in time delay in communication channel and model pa...

متن کامل

Robust Iterative Learning Controller Design for 2d Uncertain Linear Systems Subject to External Disturbances

This paper presents a robust iterative learning control (ILC) for a class of two dimensional (2D) linear systems with parametric uncertainty and considerable disturbances. The proposed control law is iteratively updated to guarantees the robust stability. Based on H∞ setting, sufficient conditions for robust monotonic convergence of the proposed scheme are presented in terms of linear matrix in...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Automatica

دوره 38  شماره 

صفحات  -

تاریخ انتشار 2002