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

In many industrial robot applications it is a fact that the robot is programmed to do the same task repeatedly. By observing the control error in the different iterations of the same task it becomes clear that it is actually highly repetitive. Iterative Learning Control (ILC) allows to iteratively compensate for and, hence, remove this repetitive error.In the thesis different aspects of iterative learning control are covered. Although stability is the most important in practice the design aspect is also highlighted. Several design schemes for iterative learning control methods are presented, including first order as well as second order iterative learning control. An adaptive approach to iterative learning control is also discussed. Many of the suggested design methods are also given with stability and robustness results.The application, industrial robot control, that has been used as a testbed throughout the thesis is described. The description includes a general discussion on robot modeling and control as well as a specific discussion on the implementation of the functions needed in the commercial robot control software in order to make it possible to apply iterative learning control.The suggested iterative learning control design methods are all tested on the robot. Some practical aspects on the path following problem for industrial robots using iterative learning control are discussed. A potential solution to the path tracking problem using additional sensors is given, although it is not yet implemented on the robot.

Iterative Learning Control : Analysis, Design, and Experiments

An optical evaluation technique is described that is suitable for determining the positions and magnitudes of reflection sites within miniature optical assemblies. This method utilizes the coherence effects exhibited by a broadband optical source and is referred to as optical coherence-domain reflectometry. Background theory is given, and experimental results have demonstrated a resolution of 10 μm with an optical dynamic range of more than 100 dB.

Optical coherence-domain reflectometry: a new optical evaluation technique

Many of the control systems used in factory production today are programmed to perform the same task repeatedly. In particular this is the case for industrial robots where the same motion is performed every time the same program is executed. An interesting observation, for the industrial robot, is that the error in the different iterations of the same exercise is highly repetitive.In the thesis Iterative Learning Control is applied to an industrial robot control system from ABB. Using Iterative Learning Control the tracking error on the motor side has been reduced without changing the internal structure or any parameters in the robot controller. The results from the experiments show that Iterative Learning Control can be used to successfully reduce the tracking error in an industrial robot control system.The implementation of the functions needed in the robot controller is described. By using a combination of already present functions in the system and software development the Iterative Learning Control method has been successfully applied to the commercial robot controller, S4C.The Iterative Learning Control method uses knowledge from previous exercises to improve the control in future executions of the same exercise. For the robot control case, this means remembering the error that was achieved in the previous iteration of the exercise and to change the input signal to the system based on this knowledge.A theory including analysis and synthesis for Iterative Learning Controlis provided, and a starting point for a more general theory on iterativesystems is given. A general discussion on how the Iterative Learning Control method can deal with repetitive and random disturbances is also included.Two of the given design algorithms are evaluated by experiments. The experiments show that the methods apply very well to the control of the ABB robot. The error reaches steady state levels, in fact the quantization level, already after 3 iterations with a model based synthesis approach.

On Analysis and Implementation of Iterative Learning Control

Off-line feed-forward and feedback control on a vibration rig

Learning control is one of the most interesting subjects in robotics field, and several works on this topic were extensively investigated. Learning control is necessary for high-speed and high-precision trajectory control in cases where an objective system includes uncertain parameters and/or has practical limitations on the feedback control. Conventional learning control methods, however, have a problem concerning how to determine a learning operator that guarantees the convergence of the scheme without a priori knowledge of an objective system. For instance, designing learning controllers that will work for complex robot systems, such as pneumatic robots with complicated dynamics or robots with complex sensory feedback, is extremely difficult.



This article provides a new type of learning control scheme for a class of discrete-time nonlinear systems. The algorithm of proposed learning control utilizes local linearization techniques by using Discrete Fourier Transform (DFT) to design the learning operator and the numerical function iterative techniques. In our case, the secant method is used, which can find the best learning operator by itself at each learning step, in other words, at each calculation step of iteration. This proposed learning algorithm has been extensively tested by simulation on the computer. © 1994 John Wiley & Sons, Inc.

Learning control based on local linearization by using DFT

The control problem of robot systems with unmodeled factors is discussed by using two examples One example of such complex systems is a robot manipulator with Rubbertuators which have complicated dynamical characteristics such as the hysterisis and the compressibility of air Another one is a multi-fingered robot hand manipulating an object under the influence of the task environment The applicability of a learning control scheme to the former and a cooperative control scheme to the latter is examined through several experiments

Cooperative and Learning Control for Complex Robot Systems

PROBLEM TO BE SOLVED: To provide a method for highly accurately measuring the temperature and distortion distribution of an optical fiber regardless of a measurement distance.SOLUTION: Rear Rayleigh scattering light measurement with OFDR (Optical Frequency Domain Reflectometry) is executed a plurality of time. Influence of phase noise of a frequency sweep continuous light source 1, occurring in each rear Rayleigh scattering light waveform obtained by each measurement, is compensated by a connected reference method. Then, by comparing a plurality of rear Rayleigh scattering light waveforms after phase noise compensation, temperature and distortion distribution can be measured with high accuracy even in a measurement distance exceeding about 1/2 of a coherence length of the frequency sweep light source.SELECTED DRAWING: Figure 2

Method and device for measuring temperature and distortion distribution

This paper reviews novel distributional measurement technologies that were recently developed for monitoring the health of optical fibers in communication systems. The development of these technologies has an extensive history. Physical phenomena of optical fibers have been exploited, and several requirements in managing telecommunication systems have been considered. However, significant progress is still required for diagnosing fibers in long-haul and access networks. In addition, these technologies may stimulate the development of distributional fiber sensors.

Recent Developments of Fiber Diagnosis Technologies in Optical Communication

PROBLEM TO BE SOLVED: To provide a mode coupling ratio distribution measuring apparatus that has measuring means aggregated on one side of a mode multiplex optical fiber transmission line multiplexing a plurality of modes, and can perform measurement superior in dynamic range than a conventional method, and also accurately grasp even a mode coupling ratio at a mode conversion point where different kinds of optical fiber are connected.SOLUTION: A mode coupling ratio distribution measuring apparatus 301 has a frequency difference corresponding to a Brillouin frequency shift of an optical fiber 250 to be measured as a number-mode optical fiber multiplexing a plurality of modes at one end of the optical fiber 250 to be measured, and is configured to make probe light pulses given a predetermined time delay and pump light pulse incident at a predetermined mode excitation ratio, and to receive rear Rayleigh scattered light of probe light pulses output from one end of the optical fiber 250 to be measured and Brillouin-amplified with pump light through mode selection.SELECTED DRAWING: Figure 1

Manabe Tetsuya

Papers

Learning control based on local linearization by using DFT

Cooperative and Learning Control for Complex Robot Systems

Method and device for measuring temperature and distortion distribution

Recent Developments of Fiber Diagnosis Technologies in Optical Communication

Mode coupling ratio distribution measuring method and mode coupling ratio distribution measuring apparatus