Abstract
A novel wavelet-based iterative learning control (WILC) algorithm is proposed for a class of nonlinear time-varying systems performing repetitive task with initial state error, input disturbance and output measurement noise. This novel learning control algorithm include three main terms: a feedforward high order P-type learning term (P-type ILC) is updated by more than one past control and error data in the previous trials, a feedback control term with current cycle error (CCR), and the wavelet transform term of the previous cycle error (PCR). The effectiveness of the proposed WILC is validated through numerical simulations. It is shown that this novel WILC algorithm can improve the learning rate significantly.
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