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Model predictive control: Theory and practice—A survey


M. Morari, C.E. Garcia, D. M. Prett

Automatica, vol. 25, no. 3, pp. 335-348

We refer to Model Predictive Control (MPC) as that family of controllers in which there is a direct use of an explicit and separately identifiable model. Control design methods based on the MPC concept have found wide acceptance in industrial applications and have been studied by academia. The reason for such popularity is the ability of MPC designs to yield high performance control systems capable of operating without expert intervention for long periods of time. In this paper the issues of importance that any control system should address are stated. MPC techniques are then reviewed in the light of these issues in order to point out their advantages in design and implementation. A number of design techniques emanating from MPC, namely Dynamic Matrix Control, Model Algorithmic Control, Inferential Control and Internal Model Control, are put in perspective with respect to each other and the relation to more traditional methods like Linear Quadratic Control is examined. The flexible constraint handling capabilities of MPC are shown to be a significant advantage in the context of the overall operating objectives of the process industries and the 1-, 2-, and ∞-norm formulations of the performance objective are discussed. The application of MPC to non-linear systems is examined and it is shown that its main attractions carry over. Finally, it is explained that though MPC is not inherently more or less robust than classical feedback, it can be adjusted more easily for robustness. Author Keywords: Computer control; predictive control; process control; quadratic programming; (constrained control)


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% Autogenerated BibTeX entry
@Article { MorGar:1989:IFA_2611,
    author={M. Morari and C.E. Garcia and D. M. Prett},
    title={{Model predictive control: Theory and practice—A survey}},
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