Learning feedforward with unmeasured performance variables
With application to a wirebonder
Maurice Poot (Eindhoven University of Technology)
Jorrit Sprik (Eindhoven University of Technology)
Matthijs Teurlings (Eindhoven University of Technology)
Wout Laarakkers (ASMPT)
Dragan Kostić (ASMPT)
Jim Portegies (Eindhoven University of Technology)
Tom Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
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Abstract
Feedforward motion control for unmeasured performance variables at the point of interest is crucial for attaining high throughput and accuracy in motion systems. The aim of this paper is to develop a data-driven approach for feedforward tuning that addresses the true performance at the point of interest. The presented approach is a novel methodology that employs rational feedforward structures for performing flexible tasks with high accuracy, in conjunction with an sensor fusion for addressing the point-of-interest. In particular, the tracking error of the unmeasured performance variable is accurately estimated by combining acceleration measurements and encoder measurements. Simulation results show that optimizing for the estimated point-of-interest error achieves similar tracking performance as optimizing for the true point-of-interest error, indicating accurate sensor-fusion estimates for feedforward control. Experimental validation demonstrates that optimizing for the estimated point-of-interest error significantly reduces the estimated point-of-interest tracking error compared to minimizing the encoder error.
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