Model predictive valve control of lung pressure profile tracking
Model predictive valve control of lung pressure profile tracking
Measuring changes in intracranial pressure (ICP) is critical for diagnosing many cerebral pathologies. However noninvasive methods require airway pressure to be precisely controlled. In clinical practice, this is currently performed by the subject breathing into a tube, attempting to follow a target pressure profile. They are assisted by an operator manually releasing airway pressure via a cap, however tracking is poor. This paper develops the first automatic solution, taking the form of model predictive control (MPC) of a variable release valve to assist the subject in tracking the target trajectory. This differs from conventional MPC since the controlled variable is a system parameter rather than an input signal. A novel identification approach for the combined lung model, muscle
dynamics and voluntary respiration time-varying system is also proposed. Numerical results validate the approach and show a 44% reduction in tracking error compared with manual assistance.
1
Thompson, Michael Callum
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Freeman, Christopher
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O'Brien, Neil
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Hughes, Ann-Marie
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Birch, Tony
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Marchbanks, Robert J.
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24 November 2022
Thompson, Michael Callum
05b051e4-3e27-4809-8723-fb54bf275c51
Freeman, Christopher
ccdd1272-cdc7-43fb-a1bb-b1ef0bdf5815
O'Brien, Neil
7856f2e1-73fc-4cb9-a1f4-9b6c8b9373e7
Hughes, Ann-Marie
11239f51-de47-4445-9a0d-5b82ddc11dea
Birch, Tony
755f2236-4c0c-49b5-9884-de4021acd42d
Marchbanks, Robert J.
1ebe90b6-cb8a-4f9e-9585-4e264a951d7f
Thompson, Michael Callum, Freeman, Christopher, O'Brien, Neil, Hughes, Ann-Marie, Birch, Tony and Marchbanks, Robert J.
(2022)
Model predictive valve control of lung pressure profile tracking.
2022 Australian & New Zealand Control Conference, The City of Gold Coast, Australia.
24 - 25 Nov 2022.
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Measuring changes in intracranial pressure (ICP) is critical for diagnosing many cerebral pathologies. However noninvasive methods require airway pressure to be precisely controlled. In clinical practice, this is currently performed by the subject breathing into a tube, attempting to follow a target pressure profile. They are assisted by an operator manually releasing airway pressure via a cap, however tracking is poor. This paper develops the first automatic solution, taking the form of model predictive control (MPC) of a variable release valve to assist the subject in tracking the target trajectory. This differs from conventional MPC since the controlled variable is a system parameter rather than an input signal. A novel identification approach for the combined lung model, muscle
dynamics and voluntary respiration time-varying system is also proposed. Numerical results validate the approach and show a 44% reduction in tracking error compared with manual assistance.
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Published date: 24 November 2022
Venue - Dates:
2022 Australian & New Zealand Control Conference, The City of Gold Coast, Australia, 2022-11-24 - 2022-11-25
Identifiers
Local EPrints ID: 472971
URI: http://eprints.soton.ac.uk/id/eprint/472971
PURE UUID: 31a693c2-611b-4437-ac40-bcb14edcbeda
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Date deposited: 06 Jan 2023 13:48
Last modified: 11 Dec 2024 02:39
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Contributors
Author:
Michael Callum Thompson
Author:
Christopher Freeman
Author:
Neil O'Brien
Author:
Tony Birch
Author:
Robert J. Marchbanks
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