This article surveyed the major results in iterative learning control (ILC) analysis and design over the past two decades. Problems in stability, performance, learning transient behavior, and robustness were discussed along with four design techniques that have emerged as among the most popular. The content of this survey was selected to provide the reader with a broad perspective of the important ideas, potential, and limitations of ILC. Indeed, the maturing field of ILC includes many results and learning algorithms beyond the scope of this survey. Though beginning its third decade of active research, the field of ILC shows no sign of slowing down.

A survey of iterative learning control

In this paper, the iterative learning control (ILC) literature published between 1998 and 2004 is categorized and discussed, extending the earlier reviews presented by two of the authors. 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.

Iterative Learning Control: Brief Survey and Categorization

This paper considers the use of matrix models and the robustness of a gradient-based iterative learning control (ILC) algorithm using both fixed learning gains and nonlinear data-dependent gains derived from parameter optimization. The philosophy of the paper is to ensure monotonic convergence with respect to the mean-square value of the error time series. The paper provides a complete and rigorous analysis for the systematic use of the well-known matrix models in ILC. Matrix models provide necessary and sufficient conditions for robust monotonic convergence. They also permit the construction of accurate sufficient frequency domain conditions for robust monotonic convergence on finite time intervals for both causal and non-causal controller dynamics. The results are compared with recently published results for robust inverse-model-based ILC algorithms and it is seen that the algorithm has the potential to improve the robustness to high-frequency modelling errors, provided that resonances within the plant bandwidth have been suppressed by feedback or series compensation. Copyright © 2008 John Wiley & Sons, Ltd.

Robust monotone gradient-based discrete-time iterative learning control

Iterative Feedback and Learning Control. Servo systems applications

This brief considers the iterative learning control (ILC) problem when tracking is only required at a subset of isolated time points along the trial duration. It presents a norm-optimal ILC solution to the problem with well-defined convergence properties, design guidelines, and supporting experimental results using an electromechanical test facility.

Norm-Optimal Iterative Learning Control With Intermediate Point Weighting: Theory, Algorithms, and Experimental Evaluation

In this paper, we investigate iterative learning control (ILC) for non-minimum phase systems from a novel viewpoint. For non-minimum phase systems, the magnitude of a desiredinput obtained by ILC using forward-time updating and Silverman's inversion are too large because of the influence of the unstable zeros. On the other hand, stable inversion constructs a bounded desired input by using non-causal inverse for non-minimum phase systems.



In this paper, we first clarify that ILC using an adjoint system achieves the desired input defined by stable inversion. Hence, ILC using an adjoint system is an effective method for the control of non-minimum phase systems with uncertainty. However, a useful convergence condition of ILC using an adjoint system was not achieved. Next, we develop a simple convergence condition in the frequency domain.

Koji Kinosita

Papers

Iterative learning control using adjoint systems and stable inversion