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Stochastic non-minimal state space control with application to automated drug delivery

Lookup NU author(s): Dr Quentin Clairon


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© 2018 IEEE. This paper shows how a standard proportionalintegral-plus controller, based on a non-minimal state space (NMSS) design, can be extended to reduce the effects of measurement noise and so yield smoother control inputs for automated drug delivery control applications. Use of a NMSS model for state variable feedback control design, in which all the states are based on the directly measured input and output variables, removes the need for state estimation. Nonetheless, a stochastic NMSS form, with a Kalman filter, can optionally be introduced to reduce the effect of measurement noise and so yield smoother control inputs. In this case, the Kalman filter attenuates measurement noise but does not address state disturbances. In this article, we propose a modification to the stochastic NMSS control system to enable the elimination of such state disturbances. This involves further extending the non-minimal state vector to include additional terms based on the innovations. We compare the deterministic, stochastic and extended stochastic NMSS controllers via a simulation of the control of anaesthesia using propofol.

Publication metadata

Author(s): Wilson ED, Clairon Q, Taylor CJ

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE)

Year of Conference: 2018

Pages: 28-34

Online publication date: 10/12/2018

Acceptance date: 02/04/2018

Publisher: IEEE


DOI: 10.1109/BIBE.2018.00014

Library holdings: Search Newcastle University Library for this item

ISBN: 9781538662168