Disturbance modeling and state estimation for offset-free predictive control with state-space process models

Tatjewski, P.

International Journal of Applied Mathematics and Computer Science, Tome 24 (2014) no. 2, pp. 313-323.

Résumé

Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the paper for deterministic constant-type external and internal disturbances (modeling errors). The application and importance of constant state disturbance prediction in the state-space MPC controller design is presented. In the case with a measured state, this leads to the control structure without disturbance state observers. In the case with an unmeasured state, a new, simpler MPC controller-observer structure is proposed, with observation of a pure process state only. The structure is not only simpler, but also with less restrictive applicability conditions than the conventional approach with extended process-and-disturbances state estimation. Theoretical analysis of the proposed structure is provided. The design approach is also applied to the case with an augmented state-space model in complete velocity form. The results are illustrated on a 2 x 2 example process problem.

Détail

Keywords: model predictive control, state space model, disturbance rejection, state observer, Kalman filter
Mots-clés : sterowanie predykcyjne, model przestrzeni stanów, eliminacja zakłóceń, obserwator stanu, filtr Kalmana

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