Real-time nonlinear MPC and MHE for a large-scale mechatronic application

Milan  Vukov; S. Gros; G. Horn; Gianluca Frison; K. Geebelen; John Bagterp Jørgensen; J. Swevers; Moritz  Diehl

doi:10.1016/j.conengprac.2015.08.012

Real-time nonlinear MPC and MHE for a large-scale mechatronic application

Milan Vukov, S. Gros, G. Horn, Gianluca Frison, K. Geebelen, John Bagterp Jørgensen, J. Swevers, Moritz Diehl

Research output: Contribution to journal › Journal article › Research › peer-review

Abstract

Progress in optimization algorithms and in computational hardware made deployment of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) possible to mechatronic applications. This paper aims to assess the computational performance of NMPC and MHE for rotational start-up of Airborne Wind Energy systems. The capabilities offered by an automatic code generation tool are experimentally verified on a real physical system, using a model comprising 27 states and 4 inputs at a sampling frequency of 25 Hz. The results show the feedback times less than 5 ms for the NMPC with more than 1500 variables.

Original language	English
Journal	Control Engineering Practice
Volume	45
Pages (from-to)	64-78
ISSN	0967-0661
DOIs	https://doi.org/10.1016/j.conengprac.2015.08.012
Publication status	Published - 2015

Keywords

Nonlinear model predictive control
Moving horizon estimation
Real-time iteration (RTI) scheme
Automatic code generation
Airborne wind energy
Rotational start-up

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10.1016/j.conengprac.2015.08.012

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title = "Real-time nonlinear MPC and MHE for a large-scale mechatronic application",

abstract = "Progress in optimization algorithms and in computational hardware made deployment of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) possible to mechatronic applications. This paper aims to assess the computational performance of NMPC and MHE for rotational start-up of Airborne Wind Energy systems. The capabilities offered by an automatic code generation tool are experimentally verified on a real physical system, using a model comprising 27 states and 4 inputs at a sampling frequency of 25 Hz. The results show the feedback times less than 5 ms for the NMPC with more than 1500 variables.",

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AU - Vukov, Milan

AU - Gros, S.

AU - Horn, G.

AU - Frison, Gianluca

AU - Geebelen, K.

AU - Jørgensen, John Bagterp

AU - Swevers, J.

AU - Diehl, Moritz

PY - 2015

Y1 - 2015

N2 - Progress in optimization algorithms and in computational hardware made deployment of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) possible to mechatronic applications. This paper aims to assess the computational performance of NMPC and MHE for rotational start-up of Airborne Wind Energy systems. The capabilities offered by an automatic code generation tool are experimentally verified on a real physical system, using a model comprising 27 states and 4 inputs at a sampling frequency of 25 Hz. The results show the feedback times less than 5 ms for the NMPC with more than 1500 variables.

AB - Progress in optimization algorithms and in computational hardware made deployment of Nonlinear Model Predictive Control (NMPC) and Moving Horizon Estimation (MHE) possible to mechatronic applications. This paper aims to assess the computational performance of NMPC and MHE for rotational start-up of Airborne Wind Energy systems. The capabilities offered by an automatic code generation tool are experimentally verified on a real physical system, using a model comprising 27 states and 4 inputs at a sampling frequency of 25 Hz. The results show the feedback times less than 5 ms for the NMPC with more than 1500 variables.

KW - Nonlinear model predictive control

KW - Moving horizon estimation

KW - Real-time iteration (RTI) scheme

KW - Automatic code generation

KW - Airborne wind energy

KW - Rotational start-up

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VL - 45

SP - 64

EP - 78

JO - Control Engineering Practice

JF - Control Engineering Practice

ER -

Real-time nonlinear MPC and MHE for a large-scale mechatronic application

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Keywords

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