<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

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

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

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Disturbance modeling and design of state estimators for offset-free Model Predictive Control (MPC) with linear state-space process models is considered in the...

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

Pubblicato online: 26 giu 2014

Pagine: 313 - 323

Ricevuto: 27 set 2013

DOI: https://doi.org/10.2478/amcs-2014-0023

Parole chiave
model predictive control, state-space models, disturbance rejection, state observer, Kalman ﬁlter

© 2014 Piotr Tatjewski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Pubblicato online: 26 giu 2014

Pagine: 313 - 323

Ricevuto: 27 set 2013

DOI: https://doi.org/10.2478/amcs-2014-0023

Parole chiavemodel predictive control, state-space models, disturbance rejection, state observer, Kalman ﬁlter

© 2014 Piotr Tatjewski

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
model predictive control, state-space models, disturbance rejection, state observer, Kalman ﬁlter