Distributed Model Predictive Control (DMPC) has received significant attention in the literature. However, the robustness of DMPC with respect to model errors has not been explicitly addressed. In this paper, an online algorithm that deals explicitly with model errors for DMPC is proposed. The algorithm requires decomposing the entire system into N subsystems and solving N convex optimization problems to minimize an upper bound on a robust performance objective by using a time-varying state-feedback controller for each subsystem. Simulations on two typical examples were considered to illustrate the application of the proposed method.