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

• Autorzy: Tatjewski P.

Identyfikatory

DOI

10.1515/acsc-2017-0035

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

nonlinear control; predictive control; offset-free control; state-space model; state estimation

• Wydawca: Polish Academy of Sciences, Committee of Automatic Control and Robotics
• Czasopismo: Archives of Control Sciences
• Rocznik: 2017
• Tom: Vol. 27, no. 4
• Strony: 595--615
• Opis fizyczny: Bibliogr. 28 poz., wykr., wzory

Twórcy

autor

Tatjewski P.

• Warsaw University of Technology, Nowowiejska 15/19, 00-665 Warszawa

Bibliografia

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Uwagi

PL

Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).