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

• Autorzy: Piotr Tatjewski
• Języki publikacji: EN

Abstrakty

EN

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 2×2 example process problem.

Słowa kluczowe

EN

model predictive control; state-space models; disturbance rejection; state observer; Kalman filter

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2014
• Tom: 24
• Numer: 2
• Strony: 313-323

Daty

wydano

2014

otrzymano

2013-09-27

poprawiono

2014-01-08

Twórcy

autor

Piotr Tatjewski

• Institute of Control and Computation Engineering, Warsaw University of Technology, ul. Nowowiejska 15/19, 00-665 Warsaw, Poland

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Identyfikatory

DOI

10.2478/amcs-2014-0023