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

Survey on iterative learning control, repetitive control, and run-to-run control

This paper proposes an adaptive control for a class of nonlinear mechanisms with guaranteed transient and steady-state performance. A performance function characterizing the convergence rate, maximum overshoot, and steady-state error is used for the output error transformation, such that stabilizing the transformed system is sufficient to achieve the tracking control of the original system with a priori prescribed performance. A continuously differentiable friction model is adopted to account for the friction nonlinearities, for which primary model parameters are online updated. A novel high-order neural network with only a scalar weight is developed to approximate unknown nonlinearities and to dramatically diminish the computational costs. Comparative experiments on a turntable servo system are included to verify the reliability and effectiveness.

Adaptive Prescribed Performance Motion Control of Servo Mechanisms with Friction Compensation

In this chapter we give an overview of the field of iterative learning control (ILC). We begin with a detailed description of the ILC technique, followed by two illustrative examples that give a flavor of the nature of ILC algorithms and their performance. This is followed by a topical classification of some of the literature of ILC and a discussion of the connection between ILC and other common control paradigms, including conventional feedback control, optimal control, adaptive control, and intelligent control. Next, we give a summary of the major algorithms, results, and applications of ILC given in the literature. This discussion also considers some emerging research topics in ILC. As an example of some of the new directions in ILC theory, we present some of our recent results that show how ILC can be used to force a desired periodic motion in an initially non-repetitive process: a gas-metal arc welding system. The chapter concludes with summary comments on the past, present, and future of ILC.

Iterative Learning Control: An Expository Overview

The convergence properties of iterative learning control (ILC) algorithms are considered. The analysis is carried out in a framework using linear iterative systems, which enables several results from the theory of linear systems to be applied. This makes it possible to analyse both first-order and high-order ILC algorithms in both the time and frequency domains. The time and frequency domain results can also be tied together in a clear way. Results are also given for the iterationvariant case, i.e. when the dynamics of the system to be controlled or the ILC algorithm itself changes from iteration to iteration.

Time and frequency domain convergence properties in iterative learning control

In this paper, we address the problem of precision motion control of permanent-magnet linear motors (PMLMs) under the influence of significant disturbances. We establish a mathematical model of a PMLM driven by a sinusoidal pulsewidth-modulated (PWM) amplifier, obtaining it from a describing function analysis of the essentially nonlinear characteristics. The overall model (PWM+PMLM) inevitably inherits uncertainties in the face of load changes, system parameter perturbation, noise, and inherent system nonlinearities, etc., all of which constitute disturbances to the control system that will adversely affect the precision and accuracy. We propose a robust control scheme employing a disturbance observer to address the sensitivity of the control performance to the disturbances. Real-time experimental results are provided to verify and confirm the practical effectiveness of the proposed approach.

Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors

In this paper, with a modest amount of modeling effort, a feedback-feedforward control structure is proposed for precision motion control of a permanent magnet linear motor for applications which are inherently repetitive in terms of the motion trajectories. First, a proportional-integral-derivative (PID) feedback controller is designed using an automatic relay tuning method. An iterative learning controller based on zero-phase filtering is applied as feedforward controller to the existing relay-tuned PID feedback controller to enhance the trajectory tracking performance by utilizing the experience gained from the repeated execution of the same operations. Experimental results are presented to demonstrate the practical appeal and effectiveness of the proposed scheme.

High precision linear motor control via relay-tuning and iterative learning based on zero-phase filtering

In this tutorial paper, we introduce the fractional order calculus and fractional order control with possible applications in biomimetic actuator control. Since purely analog type fractional order integrator can be made with certain biomimetic materials or intelligent materials in general, it is natural to advocate the concept of "intelligent control of intelligent materials by using intelligent materials"

Huifang Dou

Papers

Precision motion control with disturbance observer for pulsewidth-modulated-driven permanent-magnet linear motors

High precision linear motor control via relay-tuning and iterative learning based on zero-phase filtering

Fractional Calculus and Biomimetic Control

Iterative learning control of permanent magnet linear motor with relay automatic tuning

Iterative learning feedback control of human limbs via functional electrical stimulation