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

• Main Author: Tatjewski Piotr
• Format: Article
• Language: English
• Published: Sciendo 2014-06-01
• Series: International Journal of Applied Mathematics and Computer Science
• Subjects: model predictive control; state-space models; disturbance rejection; state observer; kalman filter
• Online Access: https://doi.org/10.2478/amcs-2014-0023

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.