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LMI-based robust model predictive control evaluated on an industrial CSTR model

In this paper, robust model predictive control (MPC) is studied for a general class of uncertain linear systems with structured time-varying uncertainties. The controller design is characterized as an optimization problem of the "worst-case" objective function over infinite moving horizon, subject to input and output constraints. A sufficient state-feedback synthesis condition is provided in the form of linear matrix inequality (LMI) optimization, and can be solved online. The stability of such a control scheme is determined by the feasibility of the optimization problem. To demonstrate its usefulness, this robust MPC technique is applied to an industrial continuous stirred tank reactor (CSTR) problem with explicit input and output constraints. Its relative merits to conventional MPC approaches are also discussed.