Details

Title

Offset-free nonlinear Model Predictive Control with state-space process models

Journal title

Archives of Control Sciences

Yearbook

2017

Numer

No 4

Publication authors

Keywords

Control and Systems Engineering ; Modelling and Simulation ; Control and Optimization

Divisions of PAS

Nauki Techniczne

Abstract

<jats:title>Abstract</jats:title><jats:p>Offset-free model predictive control (MPC) algorithms for nonlinear state-space process models, with modeling errors and under asymptotically constant external disturbances, is the subject of the paper. The main result of the paper is the presentation of a novel technique based on constant state disturbance prediction. It was introduced originally by the author for linear state-space models and is generalized to the nonlinear case in the paper. First the case with measured state is considered, in this case the technique allows to avoid disturbance estimation at all. For the cases with process outputs measured only and thus the necessity of state estimation, the technique allows the process state estimation only - as opposed to conventional approach of extended process-and-disturbance state estimation. This leads to simpler design with state observer/filter of lower order and, moreover, without the need of a decision of disturbance placement in the model (under certain restrictions), as in the conventional approach. A theoretical analysis of the proposed algorithm is provided, under applicability conditions which are weaker than in the conventional approach. The presented theory is illustrated by simulation results of nonlinear processes, showing competitiveness of the proposed algorithms.</jats:p>

Publisher

Committee of Automatic Control and Robotics PAS

Date

2017

Identifier

ISSN 1230-2384

DOI

10.1515/acsc-2017-0035

×