Designing of non-fragile robust model predictive control for constrained uncertain systems and its application in process control. (November 2020) - Hakimzadeh, Ali; Ghaffari, Valiollah
Abstract: A non-fragile robust model predictive control (RMPC) is designed in the uncertain systems under bounded control signals. To this aim, a class of the nonlinear systems with additive uncertainty is considered in its general form. The RMPC synthesis could lead to the proper selection of the controller's gains. Thus, the non-fragile RMPC design is translated into a minimization problem subjected to some constraints in terms of linear matrix inequality (LMI). Hence, the controller's gains are computed by solving such a minimization problem. In some numerical examples, the suggested non-fragile RMPC is compared with the other methods. The simulation results demonstrate the effectiveness of the proposed RMPC in comparison with similar techniques. Highlights: An uncertain system is described via the general form of the additive uncertainty. The controller constraints and fragility are simultaneously concerned in RMPC design. Non-fragile RMPC is translated into a minimization problem subjected to some LMIs. The examples show effectiveness of the proposed method over the similar techniques.