Offset-free predictive control with linear state-space process models

What is it about?

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 (or piece-wise constant) type of external and internal disturbances (modeling errors). The proposed algorithms are based on constant state disturbance prediction in the state-space MPC controller and a novel prediction correction in the case of estimated process state under unmeasured disturbances.

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Why is it important?

In the case with a measured state, the proposed MPC algorithm leads to the control structure without disturbance state observers (including Kalmam filter). In the case with not measurable state, a new, simpler MPC controller-observer structure results, 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.

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