A novel digital redesign of the analog model-reference-based decentralized adaptive controller is proposed for the sampled-data large scale system consisting of N interconnected linear subsystems with actuator faults. The adaptation of the analog controller gain is derived by using the model-reference adaptive control theory based on Lyapunov method. In this paper, it is shown that the sampled-data decentralized adaptive control system is theoretically possible to asymptotically track the desired output with a specified performance even when actuator faults occur. Then, a method of actuator fault recovery is proposed. With the estimated faults, one can use the proposed input compensation method to solve actuator faults. In this paper, we also introduce a prediction-based digital redesign method to develop the corresponding sampled-data controller for the sampled-data decentralized adaptive control systems.