Robust model predictive control of a class of uncertain nonlinear systems with application to typical CSTR problems

This paper proposes a robust predictive control approach for additive discrete time uncertain nonlinear systems. The controller design is characterized as an optimization problem of the “worst-case” objective function over an infinite moving horizon. A sufficient state feedback synthesis condition is provided in the form of a linear matrix inequality (LMI) optimization and is solved online at each time step. A few simulation examples are exploited to illustrate the effectiveness of this method. Among them are two typical CSTR problems.

► We address a robust predictive control for additive uncertain nonlinear systems.
► The problem is presented in a time varying state feedback structure.
► Synthesis conditions are provided in the form of LMI optimization problems.
► We provide some remarks on input/output constraints and the tracking problem.
► The proposed approach is applied to a typical CSTR problem.