Active Insulin Infusion Control of the Blood
Glucose Derivative
J G Chase, Z-H Lam, J-Y Lee and K-S Hwang
University of Canterbury
Dept of Mechanical Engineering
Christchurch
New Zealand
ICARCV 2002, Singapore
Silicon + Biology?
• Many biological and/or medical processes are effectively feedback
control systems or can have their function replaced by feedback systems
• New 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 active
elements and/or monitoring technology (increased information flow).
– Advanced embedded computers (DSP’s) and real time operating systems
(RTOS’s) can now handle extensive calculations and operations required.
Converging technologies enables the ability to monitor, control and/or
replace dysfunctional 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 or
greater
• Computation
– Modern embedded DSP’s are far more than
adequate
• Actuation
– Insulin pump
• All are existing and near-term technologies
• Must account for limitations of existing tech
and determine the limits where practicality
and feasibility occur together.
Diabetes
• Current Treatment = Manual Monitoring + Injection = Error Prone
Type I
Blood
Glucose
Level over
Basal
IGT, Type II
Normal
Time
Ideal curve is flat!
2-3 hours Back to Fasting Level
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
bandwidth 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).

G 
,
u t   u0 1 
 Gb 
u0  nVI b
• PD controller – controls slopes of incresing/decreasing blood sugar
level rather than actual glucose concentration
dG 

u t   u0 1  k p G  k d

dt 

Two controllers, one proportional based and the other derivative
weighted where Kp << Kd create two different approaches to control
Shape control or Magnitude control
Why “Heavy Derivative” Control?
Peak glucose level –
Proportional control is
most active here
Glucose level
is still positive.
The slope is the
highest here –
Derivative control
most active here
Slope (derivative) is negative
• Derivative control - negative slope prevents further insulin injection when the
glucose level is dropping and faster reaction to positive surge.
Control of Glucose Tolerance Test
RPC – BW = 20 min
• As sampling rate increases, the more effective the controllers
become.
• Optimal control: G is very nearly flat as desired
Insulin Infusion Rates for GTT
RPC – sensor BW = 20 min
• 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.
A More Difficult Test
• 1000 calories in 4 hours over five “meal” inputs of glucose which is
rapidly absorbed
• Inputs vary in magnitude from 50 – 400 calories
• Inputs 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 occurs 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 BW
• Optimal control very nearly flat as desired
• Simple 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 1+ hour in return to basal glucose level
Insulin Infusion Rates for Glucose Inputs
• Insulin rates are sharper and nearer injections expected as sensor BW drops
• Lower insulin rates less effective control as might be expected.
Relative Proportional Control Comparison
u(t)=Uo(1+Kp(G/Gb))
Danger @ -1.5
Death @ -3
• Relative proportional control more robust to Hypoglycemic behaviour
PD Controller against Sensor Lag
(RPC)
• GlucoWatch™ (glucose sensor) has 20 minute sensor lag
• PD Controller ROBUST against 20 minute sensor lag
• The peak is slightly increased, but less hypoglycemic response
PD Controller against Sensor Failure
• Sampling bandwidth = 20 minutes
• PD controller ROBUST against 20 minute failure
• Hypoglycemia induced for 60 minute failure
Summary & Conclusions
• Bergman equations found to very suitable for control systems approach
• Feasibility of automated insulin infusion is shown in simulation
• Basic tradeoffs between sensor BW and control efficacy delineated
• Derivative control or “control of slopes” seen to be the most effective form
of feedback 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
• Kidney Failure
• Dialysis Machine
• 67 year old Female
• High fluid levels
• 3rd day in ICU
• Hyper-insulinemic
and Hyper-glycemic
GlucoCard error = 7%