A high-gain adaptive fractional-order iterative learning control

This paper combines the fractional-order iterative learning control (FOILC) and the high-gain universal adaptive stabilizer (UAS) into a high-gain adaptive FOILC scheme, which is a feedforward-plus-feedback one. Allowing for the commonality of FOILC and UAS, the proposed scheme requires only the structural information of fractional-order systems, and the convergence condition remains the same with ILC cases. Besides, the introduce of UAS can efficiently improve the convergence speed of the optimized FOILC scheme, where the optimal FOILC is derived from a practical continuous time domain identification method. The illustrated simulations are provided to support the above concepts.