Iterative Learning Control With Optimal Feedback and Feedforward Control

Iterative learning control (ILC) is a feedforward control strategy used to improve the performance of a system that executes the same task repeatedly, but is incapable of compensating for non-repetitive disturbances. Thus a well-designed feedback controller needs to be used in combination with ILC. A robustness filter called the Q-filter is essential for the ILC system stability. The price to pay, however, is that the Q-filter makes it impossible for ILC to achieve perfect tracking of the repetitive reference or perfect cancellation of repetitive disturbances. To reduce error, it is effective to apply a pre-design feedforward control input in addition to ILC. In this paper, a simple P-type ILC is combined with an optimal feedback-feedforward control inspired by classic predictive control, so as to take advantages of each control strategy. It will be shown that the choice of the injection point of the learned ILC effort is crucial for a tradeoff between stability and performance. Therefore, the stability and performance analysis based on different injection points is studied. A systematic approach to the combined control scheme is also proposed. The combined control scheme is attractive due to its simplicity and promising performance. The effectiveness of the combined control scheme is verified by simulation results with a wafer scanner system.