This paper investigates the learning-based composite anti-disturbance control problem for the discrete-time switched systems with bumpless transfer performance under the average dwell-time (ADT) switching signals. In particular, a novel iterative learning observer is constructed for the estimations of system states and external disturbance to achieve a better convergence. Then an anti-disturbance controller is designed for the augmented system, and sufficient conditions are proposed for the exponential stability with an performance index of the resultant closed-loop system by the ADT method and Lyapunov function technique. Besides, to further suppress the control input bumps, the learning-based composite anti-disturbance control strategy is combined with the bumpless transfer technique by imposing a bumpless transfer constraint. Finally, the validity of derived results is revealed by an application to the unmanned marine vehicles.
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