Imperial College London
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ProfessorEricKerrigan

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Control and Optimization
 
 
 
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Contact

 

+44 (0)20 7594 6343e.kerrigan Website

 
 
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Assistant

 

Mrs Raluca Reynolds +44 (0)20 7594 6281

 
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Location

 

1114Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kerrigan:2021:10.1109/CDC42340.2020.9304378,
author = {Kerrigan, E and Nie, Y and Faqir, O and Kennedy, CH and Niederer, SA and Solis-Lemus, JA and Vincent, P and Williams, SE},
doi = {10.1109/CDC42340.2020.9304378},
pages = {2597--2614},
publisher = {IEEE},
title = {Direct transcription for dynamic optimization: a tutorial with a case study on dual-patient ventilation during the COVID-19 pandemic},
url = {http://dx.doi.org/10.1109/CDC42340.2020.9304378},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - A variety of optimal control, estimation, system identification and design problems can be formulated as functional optimization problems with differential equality and inequality constraints. Since these problems are infinite-dimensional and often do not have a known analytical solution, one has to resort to numerical methods to compute an approximate solution. This paper uses a unifying notation to outline some of the techniques used in the transcription step of simultaneous direct methods (which discretize-then-optimize) for solving continuous-time dynamic optimization problems. We focus on collocation, integrated residual and Runge-Kutta schemes. These transcription methods are then applied to a simulation case study to answer a question that arose during the COVID-19 pandemic, namely: If there are not enough ventilators, is it possible to ventilate more than one patient on a single ventilator? The results suggest that it is possible, in principle, to estimate individual patient parameters sufficiently accurately, using a relatively small number of flow rate measurements, without needing to disconnect a patient from the system or needing more than one flow rate sensor. We also show that it is possible to ensure that two different patients can indeed receive their desired tidal volume, by modifying the resistance experienced by the air flow to each patient and controlling the ventilator pressure.
AU  - Kerrigan,E
AU  - Nie,Y
AU  - Faqir,O
AU  - Kennedy,CH
AU  - Niederer,SA
AU  - Solis-Lemus,JA
AU  - Vincent,P
AU  - Williams,SE
DO  - 10.1109/CDC42340.2020.9304378
EP  - 2614
PB  - IEEE
PY  - 2021///
SP  - 2597
TI  - Direct transcription for dynamic optimization: a tutorial with a case study on dual-patient ventilation during the COVID-19 pandemic
UR  - http://dx.doi.org/10.1109/CDC42340.2020.9304378
UR  - http://hdl.handle.net/10044/1/83173
ER  -
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