Key result
The proposed event-triggered economic model predictive control algorithm reduced the total price of hormones by 67.15% compared to switching PID and 56.22% compared to switching MPC, while maintaining effective blood glucose control.
Why the study?
Existing artificial pancreas systems focus solely on blood glucose control performance, highlighting the need to reduce economic costs and computational burdens while maintaining control efficacy.
Effect estimate: 67.15% reduction vs PID
Absolute Event Rate: 11.53% vs 35.1%
Integrating EMPC and event-triggered control in a bi-hormonal artificial pancreas system maintains glucose control while significantly reducing hormone costs and computational burden in silico.
Reduces simulated costs and computation for bi-hormonal AP; leaves open prospective human validation.
Type 1 diabetes mellitus is a lifelong disease with very high morbidity, and its medical expenses are also very alarming. It not only seriously threatens the health of patients, but also brings heavy financial burden to their families. It is necessary to seek effective and low-cost diabetes treatment therapies. Currently, the existing artificial pancreas (AP) systems emphasize only on the blood glucose (BG) control performance. This paper attempts to reduce both the economic cost and the computational burden while guaranteeing the control performance in a bi-hormonal AP system. Since economic model predictive control (EMPC) could improve the economic profit and event-triggered control could save computational resources, they are integrated and then the integrated method is applied to the bi-hormonal AP. The proposed method achieves the desired objective: 1) it guarantees the control performance, i.e., BG is maintained within 70–180mg/dL, and the average tracking error and BG risk index are small enough (21.23 and 1.59); 2) it significantly reduces the economic cost, where the total price of the hormone is reduced by 67.15% compared with the switching PID and 56.22% compared with the switching MPC; and 3) it substantially improves the computational efficiency, where the running time is reduced by 51.14% compared with the switching EMPC. It also performs well in robustness tests. Future studies will involve the clinical evaluation of the proposed scheme.
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Ma et al. (2018) studied Type 1 diabetes mellitus (n=10). Event-Triggered Economic Model Predictive Control (ET-EMPC) vs. Switching PID and Switching MPC was evaluated on Total price of hormone (economic cost in yuan) (67.15% reduction vs PID). The proposed event-triggered economic model predictive control algorithm reduced the total price of hormones by 67.15% compared to switching PID and 56.22% compared to switching MPC, while maintaining effective blood glucose control.
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