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Setpoint variation in iterative learning schemes

Iterative Learning Control (ILC) in motion control systems is often hampered by the fact that variations in the desired setpoint trajectory are not accounted for. When the setpoint changes, different dynamics are excited causing the learned signal to be less effective in reducing the tracking error. In this paper, two methods to deal with setpoint variation are discussed. In the first method, the learned feedforward signal is decomposed in different force tables corresponding to particular parts of the setpoint. The second method utilizes the learned data from different setpoints to create a finite impulse response (FIR) mapping between setpoint and feedforward input. The potential of both methods is demonstrated through experiment on a lithographic motion system.