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

A lithographic apparatus configured to project a patterned beam of radiation onto a target portion of a substrate is disclosed. The apparatus includes a first radiation dose detector and a second radiation dose detector, each detector comprising a secondary electron emission surface configured to receive a radiation flux and to emit secondary electrons due to the receipt of the radiation flux, the first radiation dose detector located upstream with respect to the second radiation dose detector viewed with respect to a direction of radiation transmission, and a meter, connected to each detector, to detect a current or voltage resulting from the secondary electron emission from the respective electron emission surface.

Lithographic apparatus, and device manufacturing method

When performing periodic tasks, the modeling uncertainties will also present some periodicity. In this paper, by appropriately applying Fourier series approximation, a practical nonlinear adaptive repetitive controller is proposed for motion control of hydraulic servomechanisms to learn and compensate the periodic modeling uncertainties. Robust control term is also constructed to effectively attenuate the effect of approximation errors, and thus asymptotic tracking performance is achieved. In addition, robustness is also discussed with respect to other nonperiodic disturbances, which reveals a guaranteed transient performance and steady-state tracking accuracy can be achieved by the proposed controller with a practical assumption. Compared to the traditional repetitive controllers, the major advantage of this controller is that it not only requires little exact knowledge of the system dynamic structure or its parameters, but also greatly reduces the noise sensitivity and heavy memory requirements. Comparative experimental results are obtained to verify the high accuracy tracking performance of the proposed control strategy.

A Practical Nonlinear Adaptive Control of Hydraulic Servomechanisms With Periodic-Like Disturbances

http://www.mate.tue.nl/mate/pdfs/11373.pdf

MIMO feed-forward design in wafer scanners using a gradient approximation-based algorithm

A unified data-driven design framework of optimality-based generalized iterative learning control

Iterative learning control (ILC) is a very effective technique to reduce systematic errors that occur in systems that repetitively perform the same motion or operation. However, several characteristics have prevented standard ILC from being widely used for high precision motion systems. Most importantly, the learned feedforward signal depends on the motion profile (setpoint trajectory) and if this is altered, the learning process has to be repeated. Secondly, ILC amplifies non-repetitive disturbances and noise. Finally, its performance may be limited due to position-dependent dynamics. This paper presents the design and implementation of a time-frequency adaptive ILC that is applicable for motion systems which executes the same motion or operation. It employs the same control system block diagram as standard ILC, but instead of a fixed robustness filter it uses a time-varying filter. By using the results of the time-frequency adaptive ILC, i.e., the shape of the learned feedforward signal, a ldquopiecewise ILCrdquo is proposed that leads to the design of a single learned feedforward signal suitable for different setpoints. The results are experimentally shown to work for a high precision motion system.

/pdf/adaptive-iterative-learning-control-for-high-precision-36wod32hir.pdf

Adaptive Iterative Learning Control for High Precision Motion Systems

By applying time-frequency analysis to a given standard iterative learning control or ILC an adaptive filter for the learned feed-forward loop is designed. This time varying filter varies according to the momentary frequency content of the error signal and allows to discriminate between areas of deterministic and stochastic error. Its application results in selective application of ILC to those intervals where error signals of high level are concentrated and allows application of a single ILC acquisition to different setpoint trajectories. The adaptive filter finds particular use in lithographic scanning systems where it is used for varying scan length.

Method of adaptive interactive learning control and a lithographic manufacturing process and apparatus employing such a method

Iterative learning control (ILC) is a known technique for improving the performance of systems or processes that operate repetitively over a fixed time interval. ILC generates a feed-forward signal effective for providing good tracking control. Experience with ILC algorithm applied to the wafer stage of a wafer scanner motion system has shown that ILC has liability to deal with limited performance in the face of position dependent dynamics, with the fact that ILC does not account for setpoint trajectory changes and with stochastic effects. The goal of the research presented in this paper is to integrate ILC applied to the wafer stage motion system with time-frequency analysis. This provides insight into the above mentioned ILC shortcomings when the learning control technique is applied on the considered motion system. We examine the suitability of a time-frequency adaptive filtering design for the learned feed-forward when applied to the wafer stage setup.

Time-frequency analysis of a motion system with learning control

A new prototype adaptive deformable mirror for future AO-systems is presented that consists of a thin continuous membrane on which push-pull actuators impose out-of-plane displacements. Each actuator has ±10?m stroke, nanometer resolution and only mW’s heat dissipation. The mirror’s modular design makes the mechanics, electronics and control system extendable towards large numbers of actuators. Models of the mirror are derived that are validated using influence and transfer function measurements. First results of a prototype with 427 actuators are also presented.

/pdf/validation-of-a-new-adaptive-deformable-mirror-concept-3ufre2qbze.pdf

Validation of a new adaptive deformable mirror concept

Future large optical telescopes require adaptive optics (AO) systems whose deformable mirrors (DM) have ever more degrees of freedom. This paper describes advances that are made in a project aimed to design a new AO system that is extendible to meet tomorrow's specifications. Advances on the mechanical design are reported in a companion paper [6272-75], whereas this paper discusses the controller design aspects. The numerical complexity of controller designs often used for AO scales with the fourth power in the diameter of the telescope's primary mirror. For future large telescopes this will undoubtedly become a critical aspect. This paper demonstrates the feasibility of solving this issue with a distributed controller design. A distributed framework will be introduced in which each actuator has a separate processor that can communicate with a few direct neighbors. First, the DM will be modeled and shown to be compatible with the framework. Then, adaptive turbulence models that fit the framework will be shown to adequately capture the spatio-temporal behavior of the atmospheric disturbance, constituting a first step towards a distributed optimal control. Finally, the wavefront reconstruction step is fitted into the distributed framework such that the computational complexity for each processor increases only linearly with the telescope diameter.

/pdf/distributed-control-in-adaptive-optics-deformable-mirror-and-476rgvcgai.pdf

Rogier Ellenbroek

Papers

Adaptive Iterative Learning Control for High Precision Motion Systems

Method of adaptive interactive learning control and a lithographic manufacturing process and apparatus employing such a method

Time-frequency analysis of a motion system with learning control

Validation of a new adaptive deformable mirror concept

Distributed control in adaptive optics: Deformable mirror and turbulence modeling