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Paper details
Number 2 - June 2014
Volume 24 - 2014
Disturbance modeling and state estimation for offset-free predictive control with state-space process models
Piotr Tatjewski
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 x 2 example process problem.
Keywords
model predictive control, state-space models, disturbance rejection, state observer, Kalman filter