Abstract
In this paper, the robust Model Predictive Control (MPC) of systems with model uncertainty is addressed. The robust approach usually involves the inclusion of nonlinear constraints to the optimization problem upon which the controller is based. At each time step the sequence of control actions is then calculated through the resolution of a NonLinear Programming problem, which can be too computer demanding for high dimension systems. Here, the conventional Multi-model Predictive Control (MMPC) problem is re-casted as an LMI-based problem that can be solved with a lower computational cost. The conventional and LMI-based robust controllers’ performances and computational costs are compared through simulations of the control of an industrial C3/C4 splitter.
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