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The implementation of lightweight high-performance motion systems in lithography applications imposes among others lower requirements on actuators, amplifiers, and cooling. However, the decreased stiffness of lightweight designs brings the effect of structural flexibilities to the fore especially when the so-called point of interest is not at a fixed location. This is for example the case when exposing...
Iterative Learning Control (ILC) enables performance improvement by learning from previous tasks. The aim of this paper is to develop an ILC approach for Linear Parameter Varying (LPV) systems to enable improved performance and increased convergence speed compared to the linear time-invariant approach. This is achieved through dedicated analysis and norm-optimal synthesis of LPV learning filters....
With disturbance feedforward compensation (DFC), input disturbances are measured and compensated to cancel the effect of the disturbance. Perfect cancellation is not possible in practice due to the causal nature of DFC, in which the compensation generally comes too late. Therefore, non-perfect plant inversion, controller discretization and sensor dynamics lead to a non-zero residual error. The properties...
This paper presents an Iterative Learning Control algorithm for direct-drive robots. The learning algorithm assumes linear dynamics, which is created using a nonlinear model-based compensator. The convergence criterion of the learning controller is derived in the frequency domain. Rules for designing the filters, used in the update law, are explained. The effectiveness of the algorithm is demonstrated...
Iterative learning control (ILC) is a control technique for systems subject to repetitive setpoints or disturbances. However, in many applications, the setpoint is not strictly repetitive, and the learning process should start all over from the beginning if the setpoint changes. In this brief, point-to-point movements with different magnitudes will be considered, which are constructed by scaling a...
Feedforward control can significantly improve the performance of a motion system through compensation of known disturbances. Recently, new feedforward algorithms have been proposed that exploit measured data from previous tasks and a suitable feedforward parametrization to attain high performance. The aim of this paper is to analyze the accuracy of these approaches. To achieve this, related results...
This paper presents a practical extension to the classical gradient-based extremum seeking control for the case when the disturbances responsible for the changes in the extremum of a related performance function can be measured. The additional information is used to improve accuracy, convergence speed and robustness of the underlying ESC scheme. Based on the disturbance measurements a map between...
Since performance variables cannot be measured directly, Iterative Learning Control (ILC) is usually applied to measured variables. In this paper, it is shown that this can deteriorate performance. New batch-wise sensors that measure the performance variables directly are well-suited for use in ILC and can potentially improve performance. In this paper, recent developments in inferential control are...
In high-performance motion systems, e.g. wafer-stages or pick-and-place machines, there is an increasing demand for higher throughput and accuracy. In the current design paradigm, i.e. rigid-body design, higher demands for throughput and accuracy will lead to a heavier machine. This paradigm does not scale anymore with higher throughputs, i.e. a new paradigm is required. The new paradigm is to design...
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