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    Active Insulin Infusion Control of the Blood Glucose Derivative.ppt

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    Active Insulin Infusion Control of the Blood Glucose Derivative.ppt

    1、Active Insulin Infusion Control of the Blood Glucose Derivative,J G Chase, Z-H Lam, J-Y Lee and K-S HwangUniversity of Canterbury Dept of Mechanical Engineering Christchurch New ZealandICARCV 2002, Singapore,Silicon + Biology?,Many biological and/or medical processes are effectively feedback control

    2、 systems or can have their function replaced by feedback systemsNew technology creating new possibilities: “BioMEMS” such as “wet sensors” and “gene chips”, are opening the path to real time physiological monitoring/sensing and actuation. Wireless technology (LAN and PAN) for communication between a

    3、ctive elements and/or monitoring technology (increased information flow). Advanced embedded computers (DSPs) and real time operating systems (RTOSs) can now handle extensive calculations and operations required.Converging technologies enables the ability to monitor, control and/or replace dysfunctio

    4、nal physiological behaviour(s).,Can the increased information from real-time sensing coupled with feedback control outperform the foreknowledge and intuition of an experienced diabetic?,3 Elements of Control Systems,Sensing Real-time sensing from GlucoWatch or similar technology at BW = 20 minutes o

    5、r greaterComputation Modern embedded DSPs are far more than adequateActuation Insulin pumpAll are existing and near-term technologiesMust account for limitations of existing tech and determine the limits where practicality and feasibility occur together.,Diabetes,Current Treatment = Manual Monitorin

    6、g + Injection = Error Prone,Diabetes is reaching epidemic proportions, treatment is dependent on unreliable individuals and has not changed significantly in 30+ years,GOALS: Automate the “5:95” (1 day every 3 weeks is “bad”) Account for variations in patient response, insulin employed, sensor bandwi

    7、dth and actuator dynamics/limits.,System Model,System model is constructed in MATLAB/Simulink. Three parts: one part for each equation in model and controller. Controller: input - G and dG/dt (GlucoWatch) and output u(t) (Insulin Pump),Controllers,Relative proportional controller (RPC).,PD controlle

    8、r controls slopes of incresing/decreasing blood sugar level rather than actual glucose concentration,Two controllers, one proportional based and the other derivative weighted where Kp Kd create two different approaches to controlShape control or Magnitude control,Why “Heavy Derivative” Control?,Deri

    9、vative control - negative slope prevents further insulin injection when the glucose level is dropping and faster reaction to positive surge.,Control of Glucose Tolerance Test,As sampling rate increases, the more effective the controllers become. Optimal control: G is very nearly flat as desired,RPC

    10、BW = 20 min,Insulin Infusion Rates for GTT,PD controller minics what a diabetic would usally do, a routine optimised over 70 years of clinical treatment. Insulin rates are sharper and nearer injections as sensor BW drops.,RPC sensor BW = 20 min,A More Difficult Test,1000 calories in 4 hours over fiv

    11、e “meal” inputs of glucose which is rapidly absorbedInputs vary in magnitude from 50 400 caloriesInputs occur in two groups of rapid succession at t = 0, 10, 30 minutes and at t = 210 and 300 minutes The last meal is 40 calories from 980 1020 calories so the full absorption of about 1000 calories oc

    12、curs by 4 hours quite easily.Controller has no knowledge of glucose input except in optimal case Input knowledge is not currently practicable in any way for this system in general,The goal is to “hammer” the system and see if it breaks!,Control of Glucose Inputs,Glucose excursions shrink with sensor

    13、 BWOptimal control very nearly flat as desiredSimple PD control emphasizes derivative over proportional inputs by 100,Normal and Diabetic Glucose Response,Response of a normal subject to Glucose Input (orange) PD controller developed is slightly better than normal subject by 7-25% on peak value and

    14、1+ hour in return to basal glucose level,Insulin Infusion Rates for Glucose Inputs,u(t)=Uo(1+Kp(G/Gb),Relative Proportional Control Comparison,Relative proportional control more robust to Hypoglycemic behaviour,Danger -1.5,Death -3,PD Controller against Sensor Lag,GlucoWatch (glucose sensor) has 20

    15、minute sensor lagPD Controller ROBUST against 20 minute sensor lagThe peak is slightly increased, but less hypoglycemic response,(RPC),PD Controller against Sensor Failure,Sampling bandwidth = 20 minutesPD controller ROBUST against 20 minute failure Hypoglycemia induced for 60 minute failure,Summary

    16、 & Conclusions,Bergman equations found to very suitable for control systems approachFeasibility of automated insulin infusion is shown in simulationBasic tradeoffs between sensor BW and control efficacy delineatedDerivative control or “control of slopes” seen to be the most effective form of feedbac

    17、k so far versus proporational dominated or relative proportional.Insulin inputs with derivative control trending towards matching those of “optimized” insulin injection regimes followed by diabetics.,Ongoing Future Work = First Known Trials,GlucoCard error = 7%,Kidney Failure Dialysis Machine67 year old FemaleHigh fluid levels3rd day in ICUHyper-insulinemic and Hyper-glycemic,


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