Model predictive control with learning‐type set‐point: Application to artificial pancreatic β‐cell |
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| Authors: | Youqing Wang Howard Zisser Eyal Dassau Lois Jovanovič Francis J Doyle III |
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| Affiliation: | 1. Dept. of Chemical Engineering, University of California, Santa Barbara, CA 93106;2. Biomolecular Science & Engineering Program, University of California, Santa Barbara, CA 93106;3. Sansum Diabetes Research Institute, Santa Barbara, CA 93105 |
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| Abstract: | 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. © 2009 American Institute of Chemical Engineers AIChE J, 2010 |
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| Keywords: | model predictive control iterative learning control (ILC) indirect ILC artificial pancreatic β ‐cell Type 1 diabetes mellitus |
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