This study presents a review of distributed model predictive control (DMPC) strategies followed by recent studies conducted by the authors on the robustness of these strategies to model errors and a summary of future challenges in this area. The review identifies three key challenges for the successful application of DMPC: (i) the selection of optimal control structure for DMPC; (ii) the choice of a suitable coordination strategy among the controllers; and (iii) the robustness of DMPC strategies to model errors. Then, the study summarises recent developments related to the robustness of unconstrained and constrained DMPC algorithms. For the unconstrained case, a methodology that is based on the calculation of a performance index is proposed to balance the trade-off between performance and structure simplicity in the presence of model errors. For the constrained case, a Jacobi iterative-based method is used to design a robust DMPC algorithm. The proposed techniques are illustrated through case studies involving a high purity binary distillation problem.