Author(s)

Jacob Anthony¹, Jacob Anthony¹, Ashley Dixon¹, Chung Hyun Goh^1*, Matthew Lucci²

¹Department of Mechanical Engineering, University of Texas at Tyler, Tyler, TX, USA.
²The Runatek Corporation, Dallas, TX, USA.

Although the opioid crisis is a problem worldwide, recent emerging technology has the potential of curtailing the epidemic. By administering micro-doses of medication as needed, a feedback-driven medicine pump could lessen the highs and lows associated with the formation of an addiction. The focus of this study was to develop a feedback control loop for this pump that optimizes drug concentration in the bloodstream based on set criteria. In the process of optimization of the system, the mathematical model representing the system was analyzed to find an open loop transfer function. Using this function, a PID tuner was applied to set feedback control. Both machine learning (ML) and deep learning (DL) techniques are explored to act as a classifier that aids the pump in administering doses. The setpoint concentration of medication in the patient’s bloodstream was calculated to be 7.55 mg/ml this setpoint was the basis for steady state concentration of the transfer function. When a PID tuner was added to the feedback system, the plot was optimized to satisfy the design criteria of a rise time less than 25-minutes and no more than a 5% overshoot of the setpoint concentration. Naïve Bayesian (NB), Tree and support-vector machines (SVM) classifiers achieved the best classification accuracy of 100%. A DL network was successfully developed to predict patient class. This work is the theoretical basis for developing a feedback-driven medicine pump and an algorithm that can classify patients based on their body’s metabolism that will aid the doctor in formatting the medicine pump so that the patient is receiving the proper amount of medication.

Machine Learning, PID, Medication Delivery Device

Anthony, J. , Anthony, J. , Dixon, A. , Goh, C. and Lucci, M. (2022) Feedback Control of Medication Delivery Device Using Machine Learning-Based Control Co-Design. Journal of Software Engineering and Applications, 15, 220-239. doi: 10.4236/jsea.2022.157013.