L-MPC improves blood glucose control in an artificial pancreatic beta-cell model by learning from daily repetitive meal patterns.
L-MPC shows promise in simulated glucose control; leaves open translation to human artificial pancreas trials.
A novel combination of model predictive control (MPC) and iterative learning control (ILC), referred to learning‐type MPC (L‐MPC), is proposed for closed‐loop control in an artificial pancreatic β‐cell. The main motivation for L‐MPC is the repetitive nature of glucose‐meal‐insulin dynamics over a 24‐h period. L‐MPC learns from an individual's lifestyle, inducing the control performance to improve from day to day. The proposed method is first tested on the Adult Average subject presented in the UVa/Padova diabetes simulator. After 20 days, the blood glucose concentrations can be kept within 68–145 mg/dl when the meals are repetitive. L‐MPC can produce superior control performance compared with that achieved under MPC. In addition, L‐MPC is robust to random variations in meal sizes within ±75% of the nominal value or meal timings within ±60 min. Furthermore, the robustness of L‐MPC to subject variability is validated on Adults 1–10 in the UVa/Padova simulator.
No takes yet. Share an insight, caveat, or question.
Wang et al. (2009) studied Diabetes (n=11). Learning-type model predictive control (L-MPC) vs. Model predictive control (MPC) was evaluated on Blood glucose concentrations.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: