Hysteresis Inverse Iterative Learning Control of Piezoactuators in AFM

Abstract We consider the application of iterative learning control (ILC) in which the input update law exploits an inverse model of the hysteresis behavior for piezoactuators. Compared to ILC for hysteresis that updates the control input using the measured tracking error scaled by a constant (fixed) learning gain, the proposed ILC algorithm converges more rapidly. The approach is analyzed and experimental results are presented to demonstrate the method's ability for precision output tracking.

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