Abstract

This paper proposes the robust iterative learning control (ILC) design for uncertain linear systems with time-varying delays and random packet dropouts. The packet dropout is modeled by an arbitrary stochastic sequence satisfying the Bernoulli binary distribution, which renders the ILC system to be stochastic instead of a deterministic one. The main idea of this paper is to transform the ILC design into robust stability for a two-dimensional (2D) stochastic system described by the Roesser model with a delay varying in a range. A delay-dependent stability condition, which can guarantee mean-square asymptotic stability of such a 2D stochastic system, is derived in terms of linear matrix inequalities (LMIs), and formulas can be given for the ILC law design. An example for the injection molding is given to demonstrate the effectiveness of the proposed ILC method.

Highlights

  • Iterative learning control (ILC) is an effective technique for systems that could perform the same task over a finite time interval repetitively

  • In [ ], an ILC algorithm integrated with Smith predictor for batch processes with fixed time delay is proposed and analyzed in the frequency domain; itcan obtain perfect tracking performance under certain conditions

  • Afterwards, a delay-dependent stability condition is derived in terms of linear matrix inequalities (LMIs), and formulas can be given for the ILC law design

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Summary

Introduction

Iterative learning control (ILC) is an effective technique for systems that could perform the same task over a finite time interval repetitively. ILC has been introduced to systems with time delays to improve the tracking performance. In [ ], an ILC scheme is proposed for systems with time delay and model uncertainties based on the internal model control principle. It is a challenge to design ILC for systems with time-varying delays.

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