ولی اله غفاری

In this thesis, a robust model predictive control (RMPC) would be designed for linear time-invariant (LTI) uncertain systems under constrained input signals. At first, a dynamic control system’s with additive uncertainty is considered in the general form. The system future and behavior may be predicted employing a nominal model. At some known times, the control signals are determined in such a way that a given cost function is closed to its minimum value. The control law may be represented as a state feedback form to optimize the cost function. So, the RMPC synthesis can lead to the proper choice of the feedback gains. In the end, the RMPC design would be translated to a minimization problem subjected to some constraints in terms of linear matrix inequality (LMI). The gains of the control law are computed by solving such a minimization problem. In some numerical examples, the suggested RMPC is compared with the other control methods. The simulation results demonstrate the effectiveness of the proposed control approach in comparison with similar techniques.