Abstract-In this article, a synthesis approach for robust model predictive control using linear matrix inequalities is presented. Uncertain time-varying parameters and bounded additive disturbances are explicitly taken into account in the controller design. Robust stability and constraint satisfaction are guaranteed by computing a positively invariant set containing the measured state at each sampling instant. The effectiveness of the proposed algorithm is illustrated by a simulation example.Index Terms-Robust model predictive control, uncertain time-varying parameters, bounded additive disturbances.
Data reconciliation is a mathematical approach that improves the quality of measurements by calculating the reconciled data that satisfy the process constraints. The conventional data reconciliation approach relies on the process model that contains the constant parameters. In the industrial applications, however, there are always possible variations of parameters within the system. In this paper, a new data reconciliation approach based on the partial differential equation (PDE) is developed. The proposed data reconciliation approach is experimentally applied to a case study of temperature measurements for a refinery process. The PDE-based model is employed in the formulation of the optimization problem. Unlike the conventional data reconciliation approach in which the system is assumed to be lumped, the PDE-based data reconciliation approach includes in the problem formulation the variations of parameters within the system in order to describe the real system's behaviour. The reconciled values can be computed within the computational domain so they can be used as the data for troubleshooting, equipment analysis and maintenance.
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