Industrial processes often exhibit complex nonlinear dynamics. Controlling such processes can be computationally intensive, making it advantageous to replace these nonlinear models with a series of linear models defined at various operating points. This approach reduces the computational burden while sufficiently preserving the system’s nonlinear dynamics. To enhance the robustness of this control strategy, we focus on designing a multimodel predictive controller (mMPC). The MPC cost function considers weighted model formulation and includes state constraints from all linear models. The approach is applied to control an industrial chemical reactor model and compared with multiple-model adaptive control (mMAC) implementing weighted state constraints. As a base for comparison, a nonlinear model predictive controller (nMPC), and a linear MPC that switches to the best model (sMPC) according to predefined state regions. The results demonstrate greater robustness and reduced constraint violations ofthe proposed method.