IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 37, NO. 6, NOVEMBER 2007 1099 Iterative Learning Control: Brief Survey and Categorization Hyo-Sung Ahn, YangQuan Chen, and Kevin L. Moore Abstract—In this paper, the iterative learning control (ILC) lit- erature published between 1998 and 2004 is categorized and dis- cussed, extending the earlier reviews presented by two of the au- thors. The papers includes a general introduction to ILC and a technical description of the methodology. The selected results are reviewed, and the ILC literature is categorized into subcategories within the broader division of application-focused and theory- focused results. Index Terms—Categorization, iterative learning control (ILC), literature review. I. INTRODUCTION I TERATIVE learning control (ILC) is an effective control tool for improving the transient response and tracking per- formance of uncertain dynamic systems that operate repetitively. Systems typically treated under the ILC framework are repet- itively operated dynamic systems, such as a robotic manipula- tor in a manufacturing environment or a chemical reactor in a batch processing application. The ILC notion can also be ex- tended to include periodically disturbed or periodically driven dynamic systems, where the periodicity could be time-, state-, or trajectory-dependent. More generally, the key idea of ILC can be viewed as a multipass process. Historically, the first novel idea related to a multipass control strategy can be traced back to [115], published in 1974, though the stability analysis was restricted to classical control concepts and did not explicitly cover the ILC approach. Interestingly, the essential idea of iter- ative learning was captured even before 1970, not in the archival literature, but in a U.S. patent, as explained in [65]. The purpose of this paper is to provide a summary and review of the recent trends in ILC research from both the application point of view and the theoretical point of view. We focus on the literature published between 1998 and 2004, logically extend- ing three previous surveys presented by two of the authors of the papers [62], [275], [282]. Section I continues with a general introduction to ILC and a technical description of the method- ology. In Section II, we summarize the survey methodology that we used and present selected results from recent literature. Section III is the main part of the paper, where we separate the Manuscript received July 24, 2005; revised May 4, 2006, August 7, 2006, and November 29, 2006. This paper was recommended by Associate Editor P. Horacek. H.-S. Ahn is with the Department of Mechatronics, Gwangju Institute of Science and Technology (GIST), Gwangju 500-712, Korea (e-mail: hyosung@gist.ac.kr). Y. Q. Chen is with the Center for Self-Organizing and Intelligent Systems, Department of Electrical and Computer Engineering, Utah State University, Logan, UT 84322 USA (e-mail: yqchen@ece.usu.edu). K. L. Moore is with the Division of Engineering, Colorado School of Mines, Golden, CO 80401 USA (e-mail: kmoore@mines.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TSMCC.2007.905759 literature into application-focused results and theory-focused re- sults, giving detailed subclassifications of each of these broader categories. Section IV presents some concluding remarks. A. What Is ILC? Control systems have played an increasingly important role in the development and advancement of modern civilization and technology. Control problems arise in practically all en- gineering areas and have been studied by both engineers and mathematicians. In industry, control systems are found in nu- merous applications, including quality control of manufactured systems, automation, network systems, machine tool control, space engineering, military, computer science, transportation systems, robotics, social systems, economic systems, and bi- ological/medical engineering, among others. Mathematically, control engineering includes modeling, analysis, and design of control systems. The key feature of control engineering is the use of feedback signals for performance improvement of a controlled system. The branches of current control theories are broad and include classical control, robust control, adaptive con- trol, optimal control, nonlinear control, neural network, fuzzy logic, and intelligent control. ILC is a relatively recent but well-established area of study in control theory. ILC, which can be categorized as an intel- ligent control methodology, 1 is an approach for improving the transient performance of systems that operate repetitively over a fixed time interval. Although control theory provides numerous design tools for improving the response of a dynamic system, it is not always possible to achieve desired performance require- ments, due to the presence of unmodeled dynamics or parametric uncertainties that are exhibited during actual system operation or to the lack of suitable design techniques [274]. Thus, it is not easy to achieve perfect tracking using traditional control theories. ILC is a design tool that can be used to overcome the shortcomings of traditional controller design, especially for ob- taining a desired transient response, for the special case when the system of interest operates repetitively. For such systems, ILC can often be used to achieve perfect tracking, even when the model is uncertain or unknown and we have no information about the system structure and nonlinearity. Various definitions of ILC have been given in the literature. Some of them are quoted here. 1) The learning control concept stands for the repeatability of operating a given objective system and the possibility 1 From “Defining intelligent control, report of the task force on Intelligent Control,” IEEE Control Systems Society, Panos Antsaklis, Chair, December 1993: “... intelligent control uses conventional control methods to solve lower level control problems ... conventional control is included in the area of intelli- gent control. Intelligent control attempts to build upon and enhance the conven- tional control methodologies to solve new challenging control problems... .” 1094-6977/$25.00 © 2007 IEEE Authorized licensed use limited to: Utah State University. 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