System identification and controlfor general anesthesia based onparsimonious Wiener models

167 Pages·2012·2.7 MB·English

ITLicentiatetheses
2012-007
System identification and control
for general anesthesia based on
parsimonious Wiener models
MARGARIDA MARTINS DA SILVA
UPPSALAUNIVERSITY
DepartmentofInformationTechnology

System identification and control
for general anesthesia based on
parsimonious Wiener models
MargaridaMartinsdaSilva
(cid:0)(cid:2)(cid:3)(cid:4)(cid:2)(cid:3)(cid:5)(cid:6)(cid:2)(cid:7)(cid:8)(cid:5)(cid:9)(cid:10)(cid:2)(cid:11)(cid:5)(cid:12)(cid:7)(cid:13)(cid:13)(cid:7)(cid:8)(cid:14)
October2012
DivisionofSystemsandControl
DepartmentofInformationTechnology
UppsalaUniversity
Box337
SE-75105 Uppsala
Sweden
(cid:15)(cid:12)(cid:12)(cid:16)(cid:17)(cid:18)(cid:18)(cid:19)(cid:19)(cid:19)(cid:7)(cid:5)(cid:12)(cid:7)(cid:13)(cid:13)(cid:7)(cid:8)(cid:14)(cid:18)
DissertationforthedegreeofLicentiateofPhilosophyinElectricalEngineeringwith
SpecializationinAutomaticControl
(cid:2)c MargaridaMartinsdaSilva2012
ISSN1404-5117
PrintedbytheDepartmentofInformationTechnology,UppsalaUniversity,Sweden

Abstract
The effect of anesthetics in the human body is usually described
by Wiener models. The high number of patient-dependent parame-
ters in the standard models, the poor excitatory pattern of the input
signals (administered anesthetics) and the small amount of available
input-output data make application of system identification strategies
difficult.
The idea behind this thesis is that, by reducing the number of pa-
rameterstodescribethesystem,improvedresultsmaybeachievedwhen
system identification algorithms and control strategies based on those
models are designed. The choice of the appropriate number of parame-
ters matches the parsimony principle of system identification.
The three first papers in this thesis present Wiener models with a
reduced number of parameters for the neuromuscular blockade and the
depth of anesthesia. Batch and recursive system identification algo-
rithms are presented. Taking advantage of the small number of continu-
oustimemodelparameters, adaptivecontrollersareproposedinthetwo
last papers. The controller structure combines an inversion of the static
nonlinearity of the Wiener model with a linear controller for the exactly
linearizedsystem,usingtheparameterestimatesobtainedrecursivelyby
an extended Kalman filter. The performance of the adaptive nonlinear
controllers is tested in a database of realistic patients with good results.
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Acknowledgments
First of all, I would like to thank my supervisors for their support,
guidance and inspiration, and for always being available for discussions
whenever I needed them. Each one of my supervisors had a special
contribution to my life during these years. Teresa Mendon¸ca showed me
that medicine can be fun. Torbj¨orn Wigren accepted me as an external
student in Uppsala back in 2009 (that made the difference!) and trusted
me while working independently. Alexander Medvedev taught me that
research has its ”outdoor” side.
The administrative staff of the department deserve a thanks for all
thehelpandservicetheyprovided, speciallybecauseagreatpartofthat
had to be done through emails.
Thanks also to all friends and colleagues at the Division of Systems
and Control in Uppsala, and all the GALENO group in Porto. Research
does not make any sense without team work.
I would like to express my gratitude to the Funda¸c˜ao para a Ciˆencia
eaTecnologiafortheresearchgrantSFRH/BD/60973/2009,andalsoto
the European Research Council (Advanced Grant 247035) for partially
funding the work done in this thesis. Funda¸c˜ao Calouste Gulbenkian,
Fundac¸˜ao Luso-Americana para o Desenvolvimento and Bernt Ja¨rmarks
Foundation also contributed financially to this work, which is gratefully
acknowledged.
A special thanks go to my parents, Catarina and Reinhard for un-
conditional support and love all along the PhD, and for accepting that
I cannot be physically present in Portugal, Austria and Sweden at the
same time.
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List of Papers
This thesis is based on the following papers:
I M. M. Silva, T. Wigren, and T. Mendonc¸a. Nonlinear identification
of a minimal neuromuscular blockade model in anesthesia. IEEE
Transactions on Control Systems Technology, vol. 20, no. 1, pp.
181-188, Jan. 2012.
II M. M. Silva, T. Mendonc¸a, and T. Wigren. Online nonlinear iden-
tification of the effect of drugs in anaesthesia using a minimal pa-
rameterization and BIS measurements. In Proc. American Control
Conference (ACC’10), Baltimore, Maryland, pp. 4379-4384, Jun.
30-Jul. 2, 2010.
III M. M. Silva. Prediction error identification of minimally parame-
terized Wiener models in anesthesia. In Proc. 18th IFAC World
Congress, Milan, Italy, pp. 5615-5620, Aug. 28-Sep. 2, 2011.
IV M. M. Silva, T. Mendon¸ca, and T. Wigren. Nonlinear adaptive
control of the neuromuscular blockade in anesthesia. In Proc. 50th
IEEE Conference on Decision and Control and European Control
Conference (CDC-ECC’11), Orlando, Florida, pp. 41-46, Dec. 12-
15, 2011.
V M. M. Silva, T. Wigren, and T. Mendonc¸a. Exactly linearizing
adaptivecontrolofpropofolandremifentanilusingareducedWiener
model for the depth of anesthesia, to appear in Proc. 51st IEEE
Conference on Decision and Control (CDC’12), Maui, Hawaii, Dec.
10-13, 2012.
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