Designing of non-fragile robust model predictive control for constrained uncertain systems and its application in process control. (November 2020)

• Record Type: Journal Article
• Authors: Hakimzadeh, Ali; Ghaffari, Valiollah
• Is Part Of: Journal of process control. Volume 95(2020)
• Page Start: 86
• Page End: 97
• Publication Date: 2020-11
• Subjects: Non-fragile controller design -- Robust model predictive control (RMPC) -- Uncertain systems -- Stability analysis; Process control -- Periodicals; Fabrication -- Contrôle -- Périodiques; Process control; Periodicals; Electronic journals; 660.281
• DOI: 10.1016/j.jprocont.2020.10.004
• Languages: English
• ISSNs: 0959-1524

Abstract

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.