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Safety-preserving control of systems with multiplicative model uncertainty with application to closed-loop anesthesia

University of British Columbia

To convince the public to employ new technologies in their daily life, the reliability and safety of such technologies must be demonstrated. Verification of safety is more crucial in applications which may directly jeopardize human safety. A self- driving car is an example of such a technology in which the safety of passengers and pedestrians depends on the car’s actions made based on its perception of the environment. A closed-loop drug delivery system is another example of a safety-critical technology for which there are risks of drug over/under-dosing and adverse side effects. Uncertainty is the key challenge in safety verification of these technologies. In the self-driving car example, the uncertainty is in the car’s knowledge of the environment. In the context of closed-loop drug delivery systems, uncertainty is in the patient response to drugs due to inter-patient variability (model uncertainty). Unmeasurablity of variables indicating the system’s safety (e.g. drug concentrations in the plasma) is another source of problems in safety verification of technologies such as closed-loop anesthesia. Motivated by closed-loop anesthesia, this thesis aims to develop a mathematical framework for formal safety verification and safety-preserving control of safety-critical systems with model uncertainty. This extends formal methods and existing safety-preserving control techniques to systems with a certain class of multiplicative model uncertainty. Moreover, this work proposes one of the first safety- preserving control schemes for output-feedback control systems in which safety- critical variables are not measurable. We also extend this technique to uncertain output-feedback systems. In this work, we employ the developed techniques to design and formalize a safety system for closed-loop anesthesia. Finally, we propose a novel approach to reduce conservatism of the formalized safety system by reducing model uncertainty using model falsification. The results discussed in this thesis may facilitate the process of obtaining regulatory approvals for closed-loop anesthesia, which helps the emergence of this technology (and other closed-loop drug delivery systems) in clinical environments.

Text

2019-01-03

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/68164

Electrical and Computer Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/