Most previous studies about fractional-order iterative learning control (FOILC) assume fixed pass lengths in iteration domain and identical initial condition. These fundamental preconditions may be violated in practical applications. This paper introduces a novel FOILC strategy for tracking control of fractional-order linear systems. To relax the fixed pass lengths assumption, redefined tracking error is applied to formulate control input. Meanwhile, an initial state learning algorithm is introduced to relax the identical initial condition assumption. Strict convergence analysis of the tracking error in iteration domain is given. Finally, two illustrative simulation examples are applied to verify the efficiency and applicability of the proposed algorithm.
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