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In this study issues related to applicability of Model-Based Predictive Control (MBPC) to nonlinear and complex processes are addressed. A tank system is taken as an exemplary process, and its prediction model is used for control purposes. Obtained results are applied for level control of a tank process. A Takagi-Sugeno type fuzzy neural network is used to model the nonlinear system. The obtained model is represented in state-space implementation. It is embedded into a model predictive control scheme and ensures the optimization procedure of MPC. Furthermore, thus formulated MPC strategy can be treated as a quadratic programming (QP) problem. It ensures ability to handle physical constraints of the system. Optimization objectives in MPC include minimization of the difference between the predicted and desired response trajectories, and the control effort subjected to prescribed constraints. The case study is implemented in MATLAB&Simulink environment.