This paper presents a multimodel-based minimum bias (MB), long-range predictive control algorithm that minimizes the bias in the output using a multiobjective optimization method. To enhance the robustness of the control system, the MB control scheme is extended to the multimodel- or multicontroller- based MB control scheme, which establishes several models simultaneously. Models with relatively small prediction errors are selected to form a group of acceptable models. The final control is chosen to be the weighted average of the MB controls. Models with significant or large errors are reinitialized. The proposed MB control scheme is evaluated by application to a paper machine benchmark problem and its performance is compared with that of other controllers.