Nonlinear Iterative Learning Control
A Frequency-Domain Approach for Fast Convergence and High Accuracy
Leontine Aarnoudse (Eindhoven University of Technology)
Alexey Pavlov (Norwegian University of Science and Technology (NTNU))
Tom Oomen (Eindhoven University of Technology, TU Delft - Mechanical Engineering)
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Abstract
Iterative learning control (ILC) involves a trade-off between perfect, fast attenuation of iteration-invariant disturbances and amplification of iteration-varying ones. The aim of this paper is to develop a nonlinear ILC framework that achieves fast convergence, robustness, and low converged error values in ILC. To this end, the method includes a deadzone nonlinearity in the learning update, which uses the difference in amplitude characteristics of repeating and varying disturbances to modify the learning gain for each error sample. A criterion for monotonic convergence of the nonlinear ILC algorithm is provided, which is used in combination with system measurements to select suitable design parameters. The proposed algorithm is validated using simulations, in which fast convergence to low error values is demonstrated.
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