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The control target in the looper-tension control of HRM (hot rolling mills) is to maintain looper angle and strip tension simultaneously at their desired values. However, the most difficult challenge in the controller design arises from the interaction, between strip tension and looper angle, and uncertainty coming from disturbances and a model mismatch. Recently, there has been some research investigating the potential benefits of MPC (model predictive control) for this control loop. However, most of this used constrained optimization based on nominal models, which often results in constraint violations for the uncertain case. The purpose of this paper is to investigate existing MPC algorithms for disturbances and evaluate their effectiveness for a hot rolling mill process through comparison examples.