Practical Approach to Design and Implementation of a Control

Ind. Eng. Chem. Res. 2009, 48, 6059–6067

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Practical Approach to Design and Implementation of a Control Algorithm in an Artificial Pancreatic Beta Cell Matthew W. Percival,†,‡ Eyal Dassau,†,‡ Howard Zisser,†,‡ Lois Jovanovicˇ,†,‡ and Francis J. Doyle III*,†,‡ Department of Chemical Engineering, UniVersity of California, Santa Barbara, California 93106-5080, and Sansum Diabetes Research Institute, 2219 Bath Street, Santa Barbara California 93105-4321

The rate limiting bottleneck in the development of an artificial pancreatic beta cell is currently the control algorithm used to make insulin dosing decisions. In this paper, a solution for the rapid development of a personalized control algorithm was proposed and the methodology was tested on the pilot scale using a physiological model of glucose-insulin kinetics and a hardware-in-the-loop (HIL) implementation of an artificial pancreatic beta cell. A model-based controller was developed from the identification tests and was augmented with advanced control strategies. The resulting controller improved glycemia from the open-loop case with no hypoglycemic events and a reduction in time spent hyperglycemic. Robustness issues were addressed with a 50% mismatch in insulin sensitivity from model to plant. The HIL platform provided insight into the effects of sensor drift and practicalities of continuous insulin delivery based on glucose sensor feedback. Introduction Type 1 diabetes mellitus (T1DM) is an autoimmune disease resulting in the destruction of the pancreatic beta cells. These cells secrete insulin, the hormone that normalizes plasma glucose concentrations. Persons with T1DM produce insufficient insulin for efficient regulation of glycemia, which leads to prolonged periods of hyperglycemia, which may result in micro- and macrovascular disease, resulting in blindness, amputations, heart attacks, strokes, and kidney damage.1 Optimizing glycemia in persons with T1DM consists of taking blood measurements manually and administering exogenous insulin with either multiple daily injections or continuously with an insulin pump. An individual following this therapy has to walk a fine line to avoid extremes of glucose and wide excursions that may result in two life-threatening conditions: 1. Diabetic ketoacidosis (DKA), which results from insufficient insulin to prevent the mobilization of fat beginning with lipolysis producing ketone bodies as intermediate products in the fatty acid-processing metabolic sequence. If large amounts of ketone bodies are produced, unprocessed ketone bodies will cause the blood pH to drop, leading to ketoacidosis. 2. Hypoglycemic coma, which results from hyperinsulinemia leading to decreased glycemia and subsequent decrease in consciousness. Hyperinsulinemia per se does not cause coma unless the blood glucose falls rapidly to concentrations incompatible with life. In order to reduce significantly the likelihood of the aforementioned diabetic complications, insulin therapy must be aggressive;2 however, this in turn increases the likelihood of hypoglycemia.3 In control parlance, this scenario of administering insulin to control glucose concentrations would be described as a singleinput single-output (SISO) system, and an ideal candidate for closed-loop control. The main benefits of “closing the loop” would be a reduction in the rate of diabetic complications and a less burdensome therapeutic regimen. In fact, closed-loop * To whom correspondence should be addressed. Tel.: +1 (805) 893-8133. Fax: +1 (805) 893-4731. E-mail address: frank.doyle@ icb.ucsb.edu. † University of California, Santa Barbara. ‡ Sansum Diabetes Research Institute.

control has been attempted in various forms since the 1970s.4 Some of these systems were multiple-input single-output (MISO), as they included dextrose delivery. Early systems were only suitable for use in a hospital setting because they relied upon intravenous (IV) insulin and dextrose delivery.5 Recent advances in real-time continuous glucose monitors (CGM) and continuous subcutaneous insulin infusion (CSII) pumps have meant that for the first time glycemic control via the subcutaneous (SC) route has become a possibility. The latest closed-loop control algorithms use the SC route, which is far more practical for ambulatory conditions due to the decreased risk of infection at the measurement and delivery site.6 A SISO system remains the most common approach. The development of stable glucagon analogs has led some pioneering groups to use a MISO system, with glucagon delivered to increase plasma glucose concentrations when necessary.7 Because an additional delivery system increases the complexity of the system, we have focused on delivery of insulin only. The SC route has some significant disadvantages. SC insulin delivery introduces lag times of approximately 1 h before maximal effects on the glucose concentration are seen;8 this lag time places exogenous delivery at an inherent disadvantage to the natural physiology, whereby insulin is released directly into the portal vein. Model predictive control (MPC) has been identified as a suitable control algorithm for SC insulin and has been implemented with some success by several groups.9,10 In these instances, extensive testing was carried out to determine physiological parameters which would not be practical on a large scale. In addition to this impracticality, the subject’s unique characteristics, such as insulin sensitivity, are changing over several time scales11,12 and the validity of parameters for such detailed models becomes questionable. Aggressive MPC tuning cannot, therefore, be justified. Indeed, sacrifice of certain MPC features reduces the control algorithm to proportional-integralderivative (PID) control, with parameters based on a crude model of glucose-insulin kinetics incorporating the main system dynamics.13 The advantage of conceding model accuracy for a reduction in the number of parameters is that simple tests suitable for ambulatory subjects can be designed and implemented for parameter identification.

10.1021/ie801432u CCC: $40.75  2009 American Chemical Society Published on Web 04/09/2009

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Ind. Eng. Chem. Res., Vol. 48, No. 13, 2009

pump, and the clinical software (APS)14 that would be used in a clinical trial.15 The only difference between HIL and clinical trials is that the subject with T1DM is replaced with a mathematical model. The HIL trials are of critical importance for determining the idiosyncrasies of closed-loop control with available CGMs and CSII pumps. This allows for a system validation and verification of the control algorithm and the related hardware for both regulatory requirements and for training of the attending clinicians. A schematic for the implementation of the HIL system is shown in Figure 2. Glucose values from the virtual subject are converted to electrical current. The CGM transmitter detects the current and relays the data to the receiver using radio frequency (RF) communication. Each control command is sent to the pump via the APS and converted from a continuous flow rate to a discrete number of microboluses; this information is acquired by the HIL and is passed to the virtual subject as an insulin input. Each discrete control command, whether basal or bolus, is converted to a set of microbolus infusions by the APS to be congruent with the physical capabilities of the insulin delivery pump. The goal of this paper was to demonstrate a practical method for tailoring a closed-loop control algorithm to a subject with T1DM using commercially available hardware. A simulation study and a series of HIL trials were carried out. An experimental protocol for deconvolution of meal and insulin responses was implemented in order to identify model parameters. Controllers were first developed in silico, with a view to addressing suitability depending upon subject compliance and robustness to insulin sensitivity changes. The best controller from the simulation study was then tested on the HIL system. Robustness was investigated in the HIL tests by introducing further insulin sensitivity changes. Experimental Setup Figure 1. Illustration the development of an artificial beta cell. The development process starts from a list of requirements and progresses to a product after three phases: 1. Simulation studies to design and evaluate the control methodology. 2. Hardware and software system validation and verification including evaluation of extreme scenarios. 3. Clinical studies starting with feasibility studies and evolving to full clinical trials to test both the safety and efficacy of the device. The process is evaluated and refined at each phase before proceeding further. Information from both the software and the hardware study are used to apply for clinical trials approval by the regulatory agency.

Product development steps for an artificial beta cell (ABC) are shown in Figure 1. The process starts with a set of system requirements such that the product user achieves normoglycemia. A three-phase approach is proposed to develop product: 1. Simulation study. The algorithms are tested in silico on a simulated subject. 2. Hardware-in-the-loop (HIL) study. Hardware and software are run in parallel on a simulated subject and regulatory work is pursued in order to gain approval for clinical trials. 3. Clinical study. Hardware and software are tested in a clinical environment on human subjects. Each phase has a feedback loop that allows modification and adjustments that will improve the overall product. Simulation studies are used to parse and tune proposed control algorithms. The HIL study involves implementing the algorithm using physical hardware and represents a critical milestone in the ABC product life cycle, similar to a pilot plant in the chemical engineering industry. In the case of an ABC, the HIL is a fully functional prototype that includes the CGM, the CSII

Simulated Subject. The glucose-insulin kinetic model of Hovorka et al.9 with the modifications for SC insulin absorption by Wilinska et al.16 was chosen to represent the virtual subject with T1DM. This model was considered as the best representation of a subject with T1DM because of the range of insulin effects and nonlinearities that it incorporates. The model was split into three parts, representing carbohydrate ingestion and glucose absorption through the gut, SC insulin absorption, and glucose kinetics. Because the published model shows a subject that is very sensitive to insulin, the insulin sensitivity parameters were reduced by 50% to be more representative of a typical subject with T1DM. Hardware Platform. A schematic of the HIL test environment is shown in Figure 2. The only difference to a clinical trial is the inclusion of a virtual subject with T1DM instead of a human. The ABC is a combination of (a) control algorithm that is implemented in MATLAB as part of the APS and lives in a personal computer and (b) a CGM (e.g., STS7, DexCom, Inc., San Diego, CA) and a CSII pump (e.g., OmniPod, Insulet Corp., Bedford, MA). Both the CGM and the CSII have two means of communication, one with the APS using serial protocols and another with the HIL via a data acquisition (DAQ) card to update the CGM with the current glucose level and query the pump for the latest delivery. Phase 1: Simulation Study. As a precursor to hardware trials, the simulation study allowed evaluation of methods in a “perfect” environment. This corresponded to perfect glucose sensor measurements, without measurement noise and sensor drift, obtained explicitly from the corresponding model


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Figure 2. Schematic showing the components of the hardware-in-the-loop setup. The communication links for each device are indicated. This setup allows plug-and-play of hardware, control algorithms, and subjects, both virtual and real.15

state, and continuous insulin infusion. None of the issues corresponding to hardware “break-in” and calibration were considered. In this phase the controller was tuned and features were evaluated.

accounted for in the APS. An information detailed flow diagram is presented in Figure 3.

Phase 2: Hardware-in-the-Loop StudysPilot Plant. The HIL study was the second stage of validation and verification of the proposed ABC and included a full system test. As part of the requirements for a product approval by a regulatory body, a system test is performed as if it were a clinical trial with a predefined protocol. Such a test mimics the procedures in a clinical trial, e.g., the initialization of the CGM, the insertion of the CSII pump, and the communication protocols using the clinical software. These trials were used to simulate extreme scenarios that would not normally occur in human subjects, e.g., disconnection of communication cables, extreme insulin infusion rates, and triggering errors in the hardware. This provided a systematic way to evaluate the system performance in the presence of an abnormal condition.

The large inter- and intrasubject variability of glucose-insulin kinetics commonplace in subjects with T1DM significantly reduces the utility of complex models with large numbers of parameters. The approach adopted in this paper focused on modeling for control. The goal was to identify parameters corresponding to the principal dynamics of the system only. This approach has previously been successful for IV glucoseinsulin kinetic parameters.17 The model parameters were identified using only a CGM and CSII pump and orally ingested carbohydrate (CHO) records. Calibration. The first part of the clinical trial was to determine the impulse responses of the two system inputs: orally ingested CHO and SC delivered insulin. For such a test, a steady-state initial condition is required. Steady-state glucose for a subject with T1DM is an oxymoron due to continuous time-varying processes, e.g., circadian variation in insulin sensitivity. In this protocol, however, steady-state was approximated by an overnight fast and no changes in SC insulin infusion in the past 5 h. A 25 g CHO meal was consumed, followed by 1.5 U SC insulin bolus 3 h later. Neither the consumption of CHO nor changes in insulin infusion were implemented for the next 6 h. The period after the meal and

The use of hardware and software in a system test highlight communication issues such as data packages lost, incomplete data, communication with the PC, data filtering by the CGM transmitter and receiver, and correct translation between a control command and the actual microboluses that are delivered by the pump. All of these were evaluated in the trial and were

System Identification

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Figure 3. Information flow diagram of the dynamic simulator for closed-loop control of an artificial beta cell with hardware-in-the-loop which allows plug-and-play (PnP) of simulated subjects and controllers: (PC) laptop computer; (APS)14 artificial pancreas software; (HMI) human-machine interface; (DAQ) data acquisition.15

usage (the user observes data infrequently) and not as part of a closed-loop system that is querying the device for a new reading every 5 min. The rationale for these filters is to reduce measurement noise; in this study, there was no measurement noise, so the result is that rapid glucose excursions are filtered out, as is clear in Figure 4 by comparing the simulation results with the HIL results. The difference is due to both a bias in the initial current (calibrated with a glucose value) and internal filters in the CGM (which apply unknown time-weighted averages to glucose signals). Transfer function models of first-order with integrator and time delay were proposed to model the effects of CHO and insulin. The overall model has the form Y(s) ) Figure 4. Glucose response curve to calibration procedure for simulation study and hardware study. The disparities in the glucose trajectories highlight the use of filters in the CGM that are sensitive to a rapid rate of change of glucose. (top) Glucose concentration profile from simulation and hardware trials. (bottom) Insulin delivery rate (solid line); instant of carbohydrate consumption (star).

the bolus was necessary to see the clinically relevant elements of the physiological response to a meal and a bolus, respectively. Glucose measurements were recorded by the CGM. Figure 4 shows the results of the calibration protocol for simulation and hardware trials. In CGMs multiple filters are used to reduce measurement noise, eliminate spikes, and to evaluate whether or not the current reading is a valid one. These filters are embedded in both the transmitter and the receiver unit. A CGM reading is the end result of these filters. In cases where the glucose rate of change is fast, signal damping can occur due to these filters. It should be noted that current CGMs were designed for personal

KIe-τD,Is KMe-τD,Ms UM(s) + U (s) s(τMs + 1) s(τIs + 1) I

(1)

U and Y are system inputs and outputs, respectively. K represents the process gain, τ, the process time constant, and τD, the process time delay. The subscript M refers to the meal, subscript I refers to insulin, and s is the Laplace variable. The integrator assumption is critical for data fitting but becomes invalid over larger time scales due to the insulin independent glucose disposal and insulin degradation effects in the simulation model. The model predictions are only important over the system’s bandwidth, in this case approximately 2 h, so this assumption is acceptable. It is the separation of the meal and bolus in the identification protocol that allows the parameters of structurally identical models to be identified. The 3 h of data after meal ingestion are used to identify the meal model; the 6 h of data after insulin bolus delivery are used to identify the insulin model. These models have three parameters each and were estimated with MATLAB’s system identification toolbox.18 Table 1 summarizes the parameters obtained in each of the trials.


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Table 1. Model Parameters and Coefficient of Variation for Simulation and Hardware Calibration Trials meal model

insulin model

trial

KM(mg/dL per g CHO)

τM (min)

τD,M (min)

R2 (%)

KI (mU/min per mg/dL)

τI (min)

τD,I (min)

R2 (%)

simulation hardware 1 hardware 2 hardware 3

4.2 1.6 1.6 1.5

53 61 56 55

12 19 24 14

99 99 99 99

-0.075 -0.028 -0.034 -0.027

146 130 190 200

27 32 29 47

99 99 99 99

Validation. An attempt to inValidate the proposed model should be made before implementation in a model-based controller; if the model is not invalidated, the model is considered valid. Model predictions are compared with input-output data not used in system identification. If the model predictions are sufficiently accurate, the model is considered to be validated. In the context of process development, this stage would determine whether or not the model is a suitable representation of the subject with T1DM and would serve as a safety check before implementation in a control algorithm. The protocol from the calibration day is repeated, followed later by two meals of 40 and 60 g CHO with boluses. These quantities of CHO were chosen as they represent a normal daily amount for someone following a low CHO diet typical of a subject with T1DM.19 The model predictions are compared with the validation data in Figure 5. The corrected predictions for 1 and 2 h are given because they correspond to the approximate bandwidth of the system. The correction to the prediction was calculated using a bias correction (eq 2); the bias for a prediction at time t for a time t + k in the future is the current measurement, G(t) less

Table 2. Parameters for the Feedforward-Feedback Controller Derived from Each Calibration Trial and Applied in Each Hardware Trial controller parameters trial

KC (mU/min per mg/dL)

τI (min)

τD (min)

KF (mU/min per g CHO)

simulation hardware 1 hardware 2 hardware 3

-0.31 -0.75 -0.75 -0.78

370 360 420 450

90 82 100 110

56 56 49 57

ˆ (t). The the uncorrected prediction at the current time, G ˆ (t + k), is the uncorrected corrected prediction at time t + k, G ˆ (t + k) plus the bias (eq 3). prediction, G ˆ (t) bias(t) ) G(t) - G

(2)

ˆ (t + k) ) G ˆ (t + k) + bias(t) G (3) The corrected predictions within the system bandwidth are representative of the system dynamics. Predictions eventually deteriorate due to nonlinearities and the assumption of an integrator in each of the transfer function models. In one case (Figure 5, subplot D), offset is eliminated entirely. We hypothesize that this is an artifact of a combination of sensor drift and the sensor’s internal filter. Because the key dynamics and gains of the virtual subject are captured, the models are suitable for use in a controller. The HIL trials showed that the glucose sensor has some internal filtering of the signal it receives. Although the HIL data and models are not identical, they are similar. In practice, the time and resources may not be available for repeated trials, particularly if equipment is reliable enough to produce repeatable results; therefore, because we have shown that only modest changes are apparent in repeated HIL trials, model 1 was considered representative of a model that would be obtained and is used in the following sections. Closed-Loop Controller Development and Implementation

Figure 5. (A-D) Glucose concentration profiles from simulation and hardware trials comparing sensor measurements and model predictions. (A) Simulated trial. (B-D) Hardware trials. (E) Insulin delivery (solid line) and instant of carbohydrate consumption (stars) which consists of three meals and three boluses. One bolus is delayed to duplicate the calibration trial.

A PID controller tuned with the internal model control (IMC) tuning rules20 was used to implement closed-loop control. Controller tuning is therefore reduced to one degree of freedom, τC, which is directly related to robustness. The parameters of the insulin model identified in the previous section were used to calculate the PID controller parameters. The use of feedforward control as a means to reject meal disturbances is a contentious issue due to safety concerns. In context, feedforward control requires subject-initiated meal announcement, consisting of a meal time and size, to be made. Many factors affect the accuracy of this meal announcement, (e.g., an overestimate of CHO in the meal, a delay in starting the meal), which could result in an incorrect dose of insulin. In the proposed implementation, there is no counter-regulatory measure available to counteract insulin overdose; it is therefore important that any feedforward action be conservative to account for these uncertainties.

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Figure 6. Tuning of controller (τC) and reference trajectory (τR) in silico. (left) No meal announcement. (right) Meal announcement with bolus and switching. The units of τC and τR are minutes. The best tuning for both scenarios is with τC ) 100 and τR ) 20.

Figure 7. Closed-loop results from the simulation study subject to mismatch between insulin sensitivity ((25%), meal size ((25%), and meal time ((15 min). (A) No meal announcement, changes in insulin sensitivity only. (B) Meal size, meal time, and insulin sensitivity subject to variation.

The experimental protocol used for the closed-loop trial consisted of a 24-h period from 6 a.m. to 6 a.m. Steady-state was assumed initially. Meals of 25, 40, and 60 g CHO were given at 7 a.m., 1 p.m., and 7 p.m., respectively. The development and tuning of the controller was done in silico; the best controller from the simulation study was then tested with the HIL trials. Tuning was done exclusively in silico. The robustness of the controller was tested in HIL trials by the introduction of insulin sensitivity changes representative of typical intra- and intersubject variation. The magnitude of the insulin sensitivity changes in the HIL trials was greater than those used in the simulation study. Controller Features. Because the pharmacokinetics of SC insulin are slower than that of orally ingested CHO, feedforward control has potential for achieving performance gains. A proportional feedforward controller was implemented and tuned based on the parameters of the meal model. This feedforward action was equivalent to a bolus dose of insulin at meal times. In order to avoid integral wind-up, switching strategies for suspending the controller at meal time have been studied in prior work.20 Two cases are investigated here: 1. No feedforward control. Meals are not announced. 2. Feedforward control. Meals are announced, and the feedback controller is suspended for 90 min. Controllers from the literature have suggested that the controller should target a time-varying reference trajectory.20 In this study, a reference trajectory was targeted based on the following conditions: 1. If the glucose measurement was less than the set point, the reference trajectory was the same as the set point.

2. If the glucose measurement was greater than the set point, the reference trajectory was an exponentially decaying trajectory between the current measurement and the set point; the speed of approach to the set point was defined by the parameter τR. Table 2 shows a summary of the parameters of the implemented controller. Figure 6 compares the performance of four controller tunings with and without meal announcement. Meal announcement clearly improved controller performance in terms of minimizing glucose excursions. However, the controller tuning must remain appropriate if a meal is consumed without announcement. This means that the controller should be tuned more conservatively overall. Considering these factors, the controller tuning parameter, τC, was set to 100 min and the reference trajectory parameter, τR, was set to 20 min; as shown in Figure 6, a controller tuned with these parameters avoids hypoglycemia while still avoiding sustained hyperglycemia. Robustness Analysis. Simulations to determine the controller robustness were made because the meal announcement may not always be correct and insulin sensitivity may change. Figure 7 shows the results of changes in insulin sensitivity ((25%), meal size ((25%), and meal time ((15 min). Under these conditions of uncertainty, the controller avoids hypoglycemia and recovers rapidly from brief periods of hyperglycemia. Closed-Loop Hardware Trials. The first model from the HIL calibration study formed the basis of the IMC-tuned PID controller for the closed-loop trials. The controller was implemented using the tuning parameters corresponding to “HIL1” in Table 2. The HIL trials simulated a 24-h period where three meals of 25, 40, and 60 g CHO were consumed at 7 a.m., 1 p.m., and 7 p.m., respectively. Two scenarios were evaluated: in the


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Figure 8. Closed-loop results using the controller developed from the calibration trials. The controller settings were τC ) 100 min, τR ) 20 min, and no meal announcement. (A) Nominal simulation trial. (B) Nominal hardware trial. (C) Insulin resistant hardware trial. (D) Insulin sensitive hardware trial.

first scenario, the controller operated only on feedback from glucose measurements; in the second scenario, the meal size was announced at the starting time of consumption and the controller gave feedforward action, after which the controller was suspended for 90 min. A cohort of virtual subjects was created to assess the robustness of the controller in the presence of insulin sensitivity changes. In addition to the nominal case, insulin resistance and sensitivity were created by a 50% decrease and increase in the parameters determining insulin sensitivity, respectively. This range was chosen in order to incorporate the majority of insulin sensitivity changes someone with diabetes would experience and is larger than that used in the simulation study for controller tuning. Figure 8 shows the controller performance on the HIL cohort without meal announcement. The controller corrects for hyperglycemic events and reduces the insulin delivery rate below basal before the glucose measurement is below the set point, indicating that the integral and derivative components have been correctly tuned according to the internal model. In the nominal case (panel B), glycemia remains between the desired range. In the insulin resistant case (panel C), two hyperglycemic events occurred; however, glycemia ultimately returns to the set point. In the case of insulin sensitivity (panel D), two minor hypoglycemic excursions occur, indicating that although the pump was suspended, too much insulin was delivered around the meals. In all cases, the glucose concentration is within the desired range overnight.

Figure 9 shows the controller performance on the HIL cohort with meal announcement. In the nominal case (panel B), apart from the bolus delivery at meal time, controller action is minimal, which indicates that the bolus quantity derived from the meal model is suitable for therapy. In the insulin resistant and sensitive cases (panels C and D, respectively), glycemia deviates from the set point significantly for sustained periods of time, resulting in hyperglycemia for both cases and minor hypoglycemia for the insulin sensitive case. At the end of the 24-h period, both glycemia and insulin delivery rates are close to their nominal values, which is indicative of controller stability. The performance of the controller in the two scenarios is summarized in Table 3. According to the high and low blood glucose indices (HBGI and LBGI),22 the risk ratings for hyperglycemia and hypoglycemia, respectively, are low for all trials. The total daily dose (TDD) varied by approximately 50% from nominal for the cases of insulin resistance and sensitivity. Meal announcement did not lead to any significant difference in TDD. The LBGI was slightly lower for the scenario with meal announcement. The HBGI was slightly higher in the case of meal announcement. Conclusions Through simulation and hardware trials, simple linear models with a minimal number of parameters have been shown to be suitable as a basis for short-term prediction of glycemia under nominal conditions and were subsequently used in a control

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Figure 9. Closed-loop results using the controllers developed from the calibration trials. The controller setting were τC ) 100 min, τR ) 20 min, and meal announcement with 90 min pump suspension after bolus delivery. (A) Nominal simulation trial. (B) Nominal hardware trial. (C) Insulin resistant hardware trial. (D) Insulin sensitive hardware trial.

algorithm. The controller was robust under uncertainty of a key system characteristic, namely insulin sensitivity, and successfully rejected meals with and without announcement. The instantaneous insulin sensitivity change in the robustness test represented a worst-case scenario; in reality, such a large change would occur incrementally and learning algorithms could be applied to update controller tuning parameters if improved performance was desired. Hardware trials have highlighted issues germane to the transfer of control algorithms from in silico to in vivo. Three matters of contention are measurement drift due to internal filters in the CGM, offset between blood glucose represented as the in silico reading and the actual CGM output, and the delivery of insulin in discrete quantities. Understanding these issues is critical for safe transfer of a closed-loop controller from simulation study to clinical trials. HIL studies provide the ideal intermediate step because they comprise a full system validation and verification by including all aspects of hardware and software integration. The asymmetric nature of the control problem has been highlighted by the relative difficulty of avoiding both hyperglycemia and hypoglycemia. Severe hyper- and hypoglycemia can be avoided, but at the cost of some short periods of mild hyperglycemia. In the nominal case, the feedforward control strategies reduce these periods of hyperglycemia. These periods of hyperglycemia could be avoided if an upper limit on CHO consumption was established and adhered to. However, new therapeutic rules only add to the burden on a person with T1DM

Table 3. Performance Metrics for the Closed-Loop Trialsa no meal announcement

meal announcement

trial

LBGI

HBGI

TDD (U)

LBGI

HBGI

TDD (U)

simulation HIL (nominal) HIL (resistant) HIL (sensitive)

0.36 0.17 0.23 3.6

3.7 0.78 3.1 0.66

29.6 29.0 46.2 16.6

0.18 0.02 0.02 0.23

4.3 0.48 10.2 3.1

29.0 28.7 50.3 16.6

a Low blood glucose index (LBGI) and high blood glucose index (HBGI) are described by Kovatchev et al.22 and represent daily variation. Total daily dose (TDD) is a measure of how much insulin was delivered and assesses controller efficacy.

so should be considered on a case-by-case basis. Ultimately, controller efficacy will depend upon a certain level of user compliance. A practical approach to the design of an ABC from concept through to simulation studies and HIL trials has been presented as an analogy to the chemical engineering product design methodology. HIL studies are a critical step in the development of an ABC similar to the role of a pilot plant in chemical engineering process design. Performing HIL trials also serves as a system validation and verification for the safety and efficacy of the device under investigation. This type of design is envisioned to be realized as a product in the near future. Acknowledgment This work was supported by the Juvenile Diabetes Research Foundation (JDRF) grant 22-2007-1801, the Institute for Col-


laborative Biotechnologies (ICB), and the Otis Williams Fund at the Santa Barbara Foundation. Literature Cited (1) Gerich, J. E. The importance of tight glycemic control. Am. J. Med. 2005, 118, 7. (2) Diabetes Control and Complications Trials Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N. Engl. J. Med. 1993, 329, 977. (3) Diabetes Control & Complications Trial Research Group. The absence of a glycemic threshold for the development of long-term complications: the perspective of the Diabetes Control and Complications Trial. Diabetes 1996, 45, 1289. (4) Bequette, B. W. A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technol. Ther. 2005, 7, 28. (5) Clemens, A. H.; Chang, P. H.; Myers, R. W. The development of Biostator, a glucose controlled insulin infusion system (GCIIS). Horm. Metab. Res. 1977, S7, 23. (6) Parker, R. S.; Doyle, F. J., III; Peppas, N. A. The intravenous route to blood glucose control. IEEE Eng. Med. Biol. Mag. 2001, 20, 65. (7) El-Khatib, F. H.; Jiang, J.; Damiano, E. D. Adaptive closed-loop control provides blood-glucose regulation using dual subcutaneous insulin and glucagon infusion in diabetic swine. J. Diabetes Sci. Technol. 2007, 1, 181. (8) Hovorka, R. Continuous glucose monitoring and closed-loop systems. Diabetic Med. 2005, 23, 1. (9) Hovorka, R.; Canonico, V.; Chassin, L. J.; Haueter, U.; MassiBenedetti, M.; Federici, M. O.; Pieber, T. R.; Schaller, H. C.; Schaupp, L.; Vering, T.; Wilinska, M. E. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Meas. 2004, 25, 905. (10) Magni, L.; Raimondo, D. M.; Bossi, L.; Dalla Man, C.; de Nicoloa, G.; Kovatchev, B.; Cobelli, C. Model predictive control of type 1 diabetes: an in silico trial. J. Diabetes Sci. Technol. 2007, 1, 804. (11) Jovanovic, L. Insulin therapy and algorithms for treating type 1 diabetes mellitus. In Optimizing insulin therapy in patients with diabetes; CME Activity jointly sponsored by Washington Hospital Center and MedStar Research Institute, 2002; p 13.

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ReceiVed for reView September 23, 2008 ReVised manuscript receiVed March 13, 2009 Accepted March 25, 2009 IE801432U

Practical Approach to Design and Implementation of a Control

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