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

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Stochastic non-minimal state space control with application to automated drug delivery. / Wilson, Emma Denise; Clairon, Q.; Taylor, C. James.

2018 18th IEEE International Conference on Bioinformatics and Bioengineering. IEEE, 2018. p. 28-34.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Wilson, ED, Clairon, Q & Taylor, CJ 2018, Stochastic non-minimal state space control with application to automated drug delivery. in 2018 18th IEEE International Conference on Bioinformatics and Bioengineering. IEEE, pp. 28-34, 18th IEEE International Conference on Bioinformatics and Bioengineering, Taichung, Taiwan, 29/10/18. https://doi.org/10.1109/BIBE.2018.00014

APA

Wilson, E. D., Clairon, Q., & Taylor, C. J. (2018). Stochastic non-minimal state space control with application to automated drug delivery. In 2018 18th IEEE International Conference on Bioinformatics and Bioengineering (pp. 28-34). IEEE. https://doi.org/10.1109/BIBE.2018.00014

Vancouver

Wilson ED, Clairon Q, Taylor CJ. Stochastic non-minimal state space control with application to automated drug delivery. In 2018 18th IEEE International Conference on Bioinformatics and Bioengineering. IEEE. 2018. p. 28-34 https://doi.org/10.1109/BIBE.2018.00014

Author

Wilson, Emma Denise ; Clairon, Q. ; Taylor, C. James. / Stochastic non-minimal state space control with application to automated drug delivery. 2018 18th IEEE International Conference on Bioinformatics and Bioengineering. IEEE, 2018. pp. 28-34

Bibtex

@inproceedings{06d6a319037e451ba54cf9da6fc0de9e,
title = "Stochastic non-minimal state space control with application to automated drug delivery",
abstract = "This paper shows how a standard proportional-integral-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.",
keywords = "Adaptive Treatment, Stochastic control, Kalman Filter, Non-Minimum State Space (NMSS), Proportional-Integral-Plus (PIP), Anaesthesia",
author = "Wilson, {Emma Denise} and Q. Clairon and Taylor, {C. James}",
year = "2018",
month = oct,
day = "29",
doi = "10.1109/BIBE.2018.00014",
language = "English",
pages = "28--34",
booktitle = "2018 18th IEEE International Conference on Bioinformatics and Bioengineering",
publisher = "IEEE",
note = "18th IEEE International Conference on Bioinformatics and Bioengineering ; Conference date: 29-10-2018 Through 31-10-2018",

}

RIS

TY - GEN

T1 - Stochastic non-minimal state space control with application to automated drug delivery

AU - Wilson, Emma Denise

AU - Clairon, Q.

AU - Taylor, C. James

PY - 2018/10/29

Y1 - 2018/10/29

N2 - This paper shows how a standard proportional-integral-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.

AB - This paper shows how a standard proportional-integral-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.

KW - Adaptive Treatment

KW - Stochastic control

KW - Kalman Filter

KW - Non-Minimum State Space (NMSS)

KW - Proportional-Integral-Plus (PIP)

KW - Anaesthesia

U2 - 10.1109/BIBE.2018.00014

DO - 10.1109/BIBE.2018.00014

M3 - Conference contribution/Paper

SP - 28

EP - 34

BT - 2018 18th IEEE International Conference on Bioinformatics and Bioengineering

PB - IEEE

T2 - 18th IEEE International Conference on Bioinformatics and Bioengineering

Y2 - 29 October 2018 through 31 October 2018

ER -