C code generation applied to nonlinear model predictive control for an artificial pancreas

Dimitri Boiroux; John Bagterp Jørgensen

doi:10.1109/PC.2017.7976235

C code generation applied to nonlinear model predictive control for an artificial pancreas

Dimitri Boiroux
, John Bagterp Jørgensen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

This paper presents a method to generate C code from MATLAB code applied to a nonlinear model predictive control (NMPC) algorithm. The C code generation uses the MATLAB Coder Toolbox. It can drastically reduce the time required for development compared to a manual porting of code from MATLAB to C, while ensuring a reliable and fairly optimized code. We present an application of code generation to the numerical solution of nonlinear optimal control problems (OCP). The OCP uses a sequential quadratic programming algorithm with multiple shooting and sensitivity computation. We consider the problem of glucose regulation for people with type 1 diabetes as a case study. The average computation time when using generated C code is 0.21 s (MATLAB: 1.5 s), and the maximum computation time when using generated C code is 0.97 s (MATLAB: 5.7 s). Compared to the MATLAB implementation, generated C code can run in average more than 7 times faster.

Original language	English
Title of host publication	Proceedings of the 2017 21st International Conference on Process Control
Publisher	IEEE
Publication date	2017
Pages	327-332
ISBN (Print)	978-1-5386-4011-1
DOIs	https://doi.org/10.1109/PC.2017.7976235
Publication status	Published - 2017
Event	21st International Conference on Process Control - Hotel SOREA TRIGAN Baník, Štrbské Pleso, Slovakia Duration: 6 Jun 2017 → 9 Jun 2017 Conference number: 21

Conference

Conference	21st International Conference on Process Control
Number	21
Location	Hotel SOREA TRIGAN Baník
Country/Territory	Slovakia
City	Štrbské Pleso
Period	06/06/2017 → 09/06/2017

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

MATLAB
Sensitivity
Optimization
Diabetes
Sugar
Approximation algorithms
Mathematical model

Access to Document

10.1109/PC.2017.7976235

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Cite this

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title = "C code generation applied to nonlinear model predictive control for an artificial pancreas",

abstract = "This paper presents a method to generate C code from MATLAB code applied to a nonlinear model predictive control (NMPC) algorithm. The C code generation uses the MATLAB Coder Toolbox. It can drastically reduce the time required for development compared to a manual porting of code from MATLAB to C, while ensuring a reliable and fairly optimized code. We present an application of code generation to the numerical solution of nonlinear optimal control problems (OCP). The OCP uses a sequential quadratic programming algorithm with multiple shooting and sensitivity computation. We consider the problem of glucose regulation for people with type 1 diabetes as a case study. The average computation time when using generated C code is 0.21 s (MATLAB: 1.5 s), and the maximum computation time when using generated C code is 0.97 s (MATLAB: 5.7 s). Compared to the MATLAB implementation, generated C code can run in average more than 7 times faster.",

keywords = "MATLAB, Sensitivity, Optimization, Diabetes, Sugar, Approximation algorithms, Mathematical model",

author = "Dimitri Boiroux and J{\o}rgensen, \{John Bagterp\}",

year = "2017",

doi = "10.1109/PC.2017.7976235",

language = "English",

isbn = "978-1-5386-4011-1",

pages = "327--332",

booktitle = "Proceedings of the 2017 21st International Conference on Process Control",

publisher = "IEEE",

address = "United States",

note = "21st International Conference on Process Control ; Conference date: 06-06-2017 Through 09-06-2017",

}

TY - GEN

T1 - C code generation applied to nonlinear model predictive control for an artificial pancreas

AU - Boiroux, Dimitri

AU - Jørgensen, John Bagterp

N1 - Conference code: 21

PY - 2017

Y1 - 2017

N2 - This paper presents a method to generate C code from MATLAB code applied to a nonlinear model predictive control (NMPC) algorithm. The C code generation uses the MATLAB Coder Toolbox. It can drastically reduce the time required for development compared to a manual porting of code from MATLAB to C, while ensuring a reliable and fairly optimized code. We present an application of code generation to the numerical solution of nonlinear optimal control problems (OCP). The OCP uses a sequential quadratic programming algorithm with multiple shooting and sensitivity computation. We consider the problem of glucose regulation for people with type 1 diabetes as a case study. The average computation time when using generated C code is 0.21 s (MATLAB: 1.5 s), and the maximum computation time when using generated C code is 0.97 s (MATLAB: 5.7 s). Compared to the MATLAB implementation, generated C code can run in average more than 7 times faster.

AB - This paper presents a method to generate C code from MATLAB code applied to a nonlinear model predictive control (NMPC) algorithm. The C code generation uses the MATLAB Coder Toolbox. It can drastically reduce the time required for development compared to a manual porting of code from MATLAB to C, while ensuring a reliable and fairly optimized code. We present an application of code generation to the numerical solution of nonlinear optimal control problems (OCP). The OCP uses a sequential quadratic programming algorithm with multiple shooting and sensitivity computation. We consider the problem of glucose regulation for people with type 1 diabetes as a case study. The average computation time when using generated C code is 0.21 s (MATLAB: 1.5 s), and the maximum computation time when using generated C code is 0.97 s (MATLAB: 5.7 s). Compared to the MATLAB implementation, generated C code can run in average more than 7 times faster.

KW - MATLAB

KW - Sensitivity

KW - Optimization

KW - Diabetes

KW - Sugar

KW - Approximation algorithms

KW - Mathematical model

U2 - 10.1109/PC.2017.7976235

DO - 10.1109/PC.2017.7976235

M3 - Article in proceedings

SN - 978-1-5386-4011-1

SP - 327

EP - 332

BT - Proceedings of the 2017 21st International Conference on Process Control

PB - IEEE

T2 - 21st International Conference on Process Control

Y2 - 6 June 2017 through 9 June 2017

ER -

C code generation applied to nonlinear model predictive control for an artificial pancreas

Abstract

Conference

UN SDGs

Keywords

Access to Document

OpenUrl availability

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Cite this