CN109925568A - It is a kind of based on adaptive softening because of the generalized predictive control infusion of insulin amount calculation method of substrategy - Google Patents
It is a kind of based on adaptive softening because of the generalized predictive control infusion of insulin amount calculation method of substrategy Download PDFInfo
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Abstract
The invention discloses a kind of based on adaptive softening because of the generalized predictive control infusion of insulin amount calculation method of substrategy, pass through the blood sugar for human body data of real-time monitoring, utilize the change of blood sugar in CARIMA model prediction future, then adaptive reference curve is tracked using LMS control and calculates the infusion rates of insulin pump in real time, enable insulin pump accurately to control infusion of insulin rate, reduces blood sugar for human body and fluctuate and controlled in scheduled target interval.Compared to existing GPC algorithm, the adaptive softening factor employed in the present invention can be according to the numerical values recited of different blood glucose value flexible modulation itself, to realize the control to the infusion rates of insulin pump, robustness with higher significantly improves the accuracy and validity of infusion of insulin.
Description
Technical field
The present invention relates to insulin pump infusion amounts to estimate field, is based on the adaptive softening factor more particularly, to one kind
The generalized predictive control infusion of insulin amount calculation method of strategy.
Background technique
Instantly there are about 4.22 hundred million adults to suffer from diabetes in the whole world, and causes 1,500,000 people dead every year.Type 1 diabetes,
Be otherwise known as insulin-dependent diabetes mellitus, is mainly characterized by the apoptosis and insulin point of pancreatic sites insulin secretory cell
Secrete deficiency.Although the definite inducement of type 1 diabetes is simultaneously indefinite, can effectively be controlled by daily infusion of insulin
The blood glucose level of diabetic, to improve the life quality of patient.Artificial pancreas can effectively detect diabetic
Blood glucose level, and accurately calculate the Infusion Time and infusion amount of insulin.Artificial pancreas mainly includes three parts: even
Continuous blood sugar test (CGM), intelligence control system and insulin pump.
Intelligence control system is the core of entire artificial pancreas, directly determines the accuracy and validity of glycemic control,
There are many Control Lyapunov functions in the intelligence control system of artificial pancreas, such as proportional integral derivative control algolithm, mould now
Type predictive control algorithm, fuzzy logic control algorithm and GPC algorithm etc..
It is to be widely used in industry that proportional integral derivative, which controls (Proportional Integral Derivative, PID),
The control algolithm in field.The control algolithm shows good closed loop by successful implantation into artificial pancreas system
Control effect.Intelligence control system based on PID design possesses brief structure, less parameter setting and higher robust
Property.But the PID controller in artificial pancreas needs to be added gain adjustment and preceding feedback operation, and require patient be manually entered into
Meal information.Aforesaid operations seriously limit the application of pid control algorithm, and bring more inconvenience to the glycemic control of patient.
Fuzzy logic (Fuzzy Logic, FL) control algolithm is widely used in field of intelligent control, in artificial pancreas system
It is also widely used in system.Its design principle and process mainly include the following steps: 1, by controlling clinical diabetes
The experience for treating long-term practice accumulation is summarized analysis, and expert knowledge library is established；2, with the basic theories of fuzzy mathematics and side
Method, condition, the fuzzy set representations of operation clinical treatment experience rule, and these fuzzy control rules and for information about
(such as clinical evaluation index) is as in knowledge deposit expert knowledge library；3, according to the blood glucose level data of real-time monitoring, with fuzzy
Reasoning obtains adaptable infusion of insulin dosimetry parameter.FL control algolithm relies on clinical expert that clinical diabetes are treated length
The phase Heuristics of practice accumulation is established as expert knowledge library, is converted into fuzzy logic control rule, meets the normal of clinical treatment
Rule experience is easy to be understood by clinician.But fuzzy logic control algorithm is used, it is concluding fuzzy rule and is choosing fuzzy
Subordinating degree function relies primarily on experience, have biggish subjectivity, there are fuzzy interval be not easy to divide, respond it is not prompt enough
Disadvantage, this brings very big obstruction to the development of fuzzy control and further genralrlization.
Model Predictive Control (Model Predictive Control, MPC) is in artificial pancreas intelligence control system
It is widely used.The algorithm describes the behavior of dynamical system using detailed model.Artificial pancreas intelligence based on MPC
The system of can control can accurately predict the influence having meal, and effectively adjust the blood glucose level of diabetic.But MPC is controlled
Device depends critically upon the accuracy of model.Body metabolism model is extremely complex, and calculation amount is very big, perfect still without one so far
Model is capable of the dynamic relationship of accurate description glucose-insulin.
Artificial pancreas intelligence control system design based on generalized predictive control is research hotspot instantly.Certainly as one kind
Suitable solution algorithm, generalized predictive control overcome MPC algorithm model rely on defect, and can lack primary condition and
The parameter of auto-adjustment control model in the case where System describe.Artificial pancreas intelligent controller based on GPC can pass through reading
Following change of blood sugar of the blood glucose information prediction for taking CGM to detect, and worked as by tracking reference curve and LMS control acquisition
Ideal infusion of insulin rate down.Specifically, 1. it has the following characteristics that using flat based on controlled autoregressive integral sliding
It is pre- that equal model (Controlled Auto-Regressive Integrated Moving-Average, CARIMA) carries out blood glucose
It surveys；2. the considerations of being weighted in objective function to controlling increment；3. utilizing the long-range forecast of output；4. controlling time domain length concept
It introduces.Compared to existing proportional integral derivative control algolithm, fuzzy logic control algorithm and model cootrol algorithm, Generalized Prediction control
Fixture has higher robustness, and does not need clinical medicine knowledge building expert database abundant and carry out model buildings.
The softening factor of generalized predictive control has effect important influence and meaning, when softening factor value approaches 1
When, generalized predictive control model can keep higher robustness, but system can be significantly for the tracking speed of reference curve
Decline；When softening factor value approaches 0, generalized predictive control can keep higher tracking for the tracking speed of reference curve
Speed, and rapid, sensitive reaction is made for the change of blood sugar of patient, but the robustness of control system can be substantially at this time
Degree is cut down.
Summary of the invention
In order to solve the above technical problems, technical scheme is as follows:
It is a kind of based on adaptive softening because of the generalized predictive control infusion of insulin amount calculation method of substrategy, described is wide
Adopted PREDICTIVE CONTROL infusion of insulin amount calculation method is adjusted by CARIMA model, LMS control and the adaptive softening factor
The infusion rates of insulin pump.
In a preferred solution, comprising the following steps:
The following steps are included:
S1: according to current blood glucose value, the predicted value of following change of blood sugar is obtained by CARIMA model；
S2: according to the predicted value of following change of blood sugar of S1, insulin pump is calculated by LMS control model
Control input increment；Wherein, LMS control is the adaptive softening factor using the softening factor, the adaptive softening because
Son can adjust the size of own value according to the variation of blood glucose value；
S3: increment is inputted to the control of insulin pump by CARIMA model and LMS control model and is optimized.
In a preferred solution, the S1 includes the following contents:
By CARIMA model and Diophantine equation, following equation is obtained:
Y (k+j)=Gj(z-1)Δu(k+j-1)+Fj(z-1) y (k) (j=1,2...n)
In formula, the y (k) indicates the blood glucose value at the k moment；The y (k+j) indicates the blood in k moment advanced j step
The predicted value of sugar value；Δ u (k+j-1) indicates that insulin pump inputs increment in the control at k moment；The n indicates maximum predicted
Length；The Gj(z-1) indicate insulin pump in the weight coefficient of the control input increment at k moment；The Fj(z-1) indicate
The weight coefficient of blood glucose value.
In a preferred solution, the S2 includes the following contents:
In formula, the J indicates the infusion rates of insulin pump；The n indicates prediction length；The λ (j) is indicated
Control weighted factor；The m indicates control length；The w (k+j) is expressed by following formula:
W (k+j)=W=Qy (k)+Myr(j=1,2 ..., n)
The yrIndicate reference curve；The W is expressed by following formula:
W=[w (k+1), w (k+2) ..., w (k+n)]T
The Q is expressed by following formula:
Q=[α, α2..., αn]T
The α indicates the adaptive softening factor；
The M is expressed by following formula:
M=[1- α, 1- α2..., 1- αn]T。
In a preferred solution, the adaptive softening factor includes the following contents:
Blood glucose desired value is manually set
According to current blood glucose value y (k), the irrelevance u of current blood glucose value and blood glucose desired value is calculated；
Calculate the variation delta y of current blood glucose value
Adaptive gentle factor α is calculated by irrelevance u and variation delta y.
In a preferred solution, the irrelevance u is expressed by following formula:
In a preferred solution, the variation delta y is expressed by following formula:
Δ y=y (k)-y (k-1)
The y (k-1) indicates the blood glucose value of previous moment.
In a preferred solution, the adaptive gentle factor α is expressed by following formula:
α=uΔy。
In a preferred solution, the S3 includes following sub-step:
S3.1: the prediction using the control of insulin pump input increment as the input value of CARIMA model, to change of blood sugar
Value is updated；
S3.2: the input according to the predicted value of updated change of blood sugar as LMS control model updates pancreas islet
The control of element pump inputs increment；Wherein, LMS control is the adaptive softening factor using the softening factor, and described is adaptive
The softening factor can adjust the size of own value according to the variation of blood glucose value；
S3.3: circulation executes S3.1~S3.2, realizes that the control input increment to insulin pump optimizes.
Compared with prior art, the beneficial effect of technical solution of the present invention is:
1, the present invention is compared to the control of existing proportional integral derivative, fuzzy logic control and Model Predictive Control, the present invention
With higher robustness, builds and be relatively easy to and do not need to be manually entered dining information；
2, present invention employs CARIMA prediction model, LMS control model, feedback compensation and parameter rolling optimization,
To ensure that the accuracy of prediction and the validity of control；
3, present invention uses the adaptive softening factors, when blood glucose value is relatively stable, the present invention can guarantee have compared with
High robustness；And when blood glucose value is increased or reduced rapidly, the present invention can be improved reaction sensitivity, rapidly tracking reference
Curve and the infusion rates for effectively adjusting insulin.
Detailed description of the invention
Fig. 1 is the control principle drawing of embodiment.
Fig. 2 is the schematic diagram of the adaptive softening factor of embodiment.
Fig. 3 is the experimental result of traditional GPC algorithm.
Fig. 4 is the experimental result of embodiment.
Specific embodiment
The attached figures are only used for illustrative purposes and cannot be understood as limitating the patent；
In order to better illustrate this embodiment, the certain components of attached drawing have omission, zoom in or out, and do not represent actual product
Size；
To those skilled in the art, it is to be understood that certain known features and its explanation, which may be omitted, in attached drawing
's.
The following further describes the technical solution of the present invention with reference to the accompanying drawings and examples.
Embodiment
As shown in Figure 1, it is a kind of based on adaptive softening because of the generalized predictive control infusion of insulin amount calculating side of substrategy
Method, comprising the following steps:
S1: according to the blood glucose value of current diabetic, obtain following diabetic's by CARIMA model
The predicted value of change of blood sugar；
By CARIMA model and Diophantine equation, following equation is obtained:
Y (k+j)=Gj(z-1)Δu(k+j-1)+Fj(z-1) y (k) (j=1,2...n)
In formula, y (k) indicates diabetic in the blood glucose value at k moment；Y (k+j) indicates that diabetic is super at the k moment
The predicted value of the blood glucose value of preceding j step；Δ u (k+j-1) indicates that insulin pump inputs increment in the control at k moment；N indicates maximum pre-
Survey length；Gj(z-1) indicate insulin pump in the weight coefficient of the control input increment at k moment；Fj(z-1) indicate blood glucose value power
Weight coefficient；
S2: according to the predicted value of the change of blood sugar of following diabetic of S1, pass through LMS control model meter
The control for calculating insulin pump inputs increment；Wherein LMS control is the adaptive softening factor using the softening factor；
In formula, J indicates the infusion rates of insulin pump；N indicates prediction length；λ (j) indicates control weighted factor；M is indicated
Control length；W (k+j) is expressed by following formula:
W (k+j)=W=Qy (k)+Myr(j=1,2 ..., n)
yrIndicate reference curve；W is expressed by following formula:
W=[w (k+1), w (k+2) ..., w (k+n)]T
Q is expressed by following formula:
Q=[α, α2..., αn]T
α indicates the adaptive softening factor；
M is expressed by following formula:
M=[1- α, 1- α2..., 1- αn]T；
As shown in Fig. 2, the adaptive softening factor includes the following contents:
Set blood glucose desired value
According to the blood glucose value y (k) of current diabetic, blood glucose value and the blood glucose phase of current diabetic are calculated
The irrelevance u of prestige value, irrelevance u are expressed by following formula:
The variation delta y of the blood glucose value of current diabetic is calculated, variation delta y is expressed by following formula:
Δ y=y (k)-y (k-1)；
Adaptive gentle factor α is calculated by irrelevance u and variation delta y, adaptive gentle factor α is carried out by following formula
Expression:
α=uΔy；
S3: increment is inputted to the control of insulin pump by CARIMA model and LMS control model and is optimized；
S3.1: using the control of insulin pump input increment as the input value of CARIMA model, to the blood of diabetic
The predicted value of sugar variation is updated；
S3.2: according to the predicted value of the change of blood sugar of updated diabetic as the defeated of LMS control model
Enter, updates the control input increment of insulin pump；Wherein, the reference curve that LMS control uses is adaptive reference song
Line, adaptive reference curve can be according to the slopes for adjusting reference curve the case where diabetic；
S3.3: circulation executes S3.1~S3.2, realizes that the control input increment to insulin pump optimizes.
The test environment of the present embodiment:
By the diabetes simulation treatment test software instead of zoopery of the present embodiment implantation U.S. FDA approval
In T1DMS, and algorithm is tested for the property.Software T1DMS uniquely can be used for replacing zoopery by what U.S. FDA was ratified
Treating diabetes test software.The software includes 100 virtual diabetes adult patients, 100 adolescent patients and 100
A children patient model, and provide virtual CGMS and insulin pump.In test process, it is only necessary to be implanted into control algolithm and survey
Platform is tried, selected test object simultaneously sets dining plan and monitoring index, so that it may observe the glycemic control effect of its insulin pump
Fruit.
The experimental result of the present embodiment:
As shown in Fig. 2, embodiment uses the higher adaptive softening factor when blood glucose level is located at 130mg/dl or so
Numerical value, embodiment is high for the sensibility of blood glucose fluctuation and robustness with higher；Significantly rise when blood glucose or
When decline, such as blood glucose is significantly increased to 170mg/dl or so, and embodiment can use lower adaptive softening factor value, this
When embodiment susceptibility with higher for blood glucose fluctuation, the defeated of insulin can be adjusted with fast track reference curve and rapidly
Infuse rate.
As shown in Figures 3 and 4, the teen-age experimental result of diabetes is suffered from.Fig. 3 is shown based on traditional generalized predictive control
The glycemic control effect (66%) of algorithm；Fig. 4 shows the glycemic control effect (95%) of the present embodiment.The test result is clear
Show that the present embodiment is capable of the blood glucose level of more stable diabetic relative to traditional GPC algorithm.
The terms describing the positional relationship in the drawings are only for illustration, should not be understood as the limitation to this patent；
Obviously, the above embodiment of the present invention be only to clearly illustrate example of the present invention, and not be pair
The restriction of embodiments of the present invention.For those of ordinary skill in the art, may be used also on the basis of the above description
To make other variations or changes in different ways.For example, for different situations can be set different adaptive optimizations because
Sub- computation model, as exponential model or logarithmic model are more advantageous to steady so that the adaptive softening factor more just closes patient itself
Determine blood glucose value.There is no necessity and possibility to exhaust all the enbodiments.Institute all within the spirits and principles of the present invention
Any modifications, equivalent replacements, and improvements etc. of work, should all be included in the scope of protection of the claims of the present invention.
Claims (9)
1. it is a kind of based on adaptive softening because of the generalized predictive control infusion of insulin amount calculation method of substrategy, feature exists
Pass through CARIMA model, LMS control and adaptive in, the generalized predictive control infusion of insulin amount calculation method
The infusion rates of softening factor adjusting insulin pump；The adaptive gentle factor α is expressed by following formula:
α=uΔy
The Δ y indicates variable quantity；The u indicates irrelevance.
2. generalized predictive control infusion of insulin amount calculation method according to claim 1, which is characterized in that including following
Step:
S1: according to current blood glucose value, the predicted value of following change of blood sugar is obtained by CARIMA model；
S2: according to the predicted value of following change of blood sugar of S1, the control of insulin pump is calculated by LMS control model
Input increment；Wherein, LMS control is the adaptive softening factor using the softening factor, the adaptive softening factor meeting
The size of own value is adjusted according to the variation of blood glucose value；
S3: increment is inputted to the control of insulin pump by CARIMA model and LMS control model and is optimized.
3. generalized predictive control infusion of insulin amount calculation method according to claim 2, which is characterized in that the S1
Including the following contents:
By CARIMA model and Diophantine equation, following equation is obtained:
Y (k+j)=Gj(z-1)Δu(k+j-1)+Fj(z-1) y (k) (j=1,2...n)
In formula, the y (k) indicates the blood glucose value at the k moment；The y (k+j) indicates the blood glucose value in k moment advanced j step
Predicted value；Δ u (k+j-1) indicates that insulin pump inputs increment in the control at k moment；The n indicates maximum predicted length；
The Gj(z-1) indicate insulin pump in the weight coefficient of the control input increment at k moment；The Fj(z-1) indicate blood glucose
The weight coefficient of value.
4. generalized predictive control infusion of insulin amount calculation method according to claim 3, which is characterized in that the S2
Including the following contents:
In formula, the J indicates the infusion rates of insulin pump；The n indicates prediction length；The λ (j) indicates control
Weighted factor；The m indicates control length；The w (k+j) is expressed by following formula:
W (k+j)=W=Qy (k)+Myr(j=1,2 ..., n)
The outer expression reference curve；The W is expressed by following formula:
W=[w (k+1), w (k+2) ..., w (k+n)]T
The Q is expressed by following formula:
Q=[α, α2..., αn]T
The α indicates the adaptive softening factor；
The M is expressed by following formula:
M=[1- α, 1- α2..., 1- αn]T。
5. according to claim 1 to generalized predictive control infusion of insulin amount calculation method described in any claim in 4,
It is characterized in that, the adaptive softening factor includes the following contents:
Blood glucose desired value is manually set
According to current blood glucose value y (k), the irrelevance u of current blood glucose value and blood glucose desired value is calculated；
Calculate the variation delta y of current blood glucose value
Adaptive gentle factor α is calculated by irrelevance u and variation delta y.
6. generalized predictive control infusion of insulin amount calculation method according to claim 5, which is characterized in that described is inclined
It is expressed from degree u by following formula:
7. generalized predictive control infusion of insulin amount calculation method according to claim 5, which is characterized in that the change
Change amount Δ y is expressed by following formula:
Δ y=y (k)-y (k-1)
The y (k-1) indicates the blood glucose value of previous moment.
8. according to the generalized predictive control infusion of insulin amount calculation method of claim 2,3,4,6 or 7, which is characterized in that institute
The S3 stated includes following sub-step:
S3.1: using the control of insulin pump input increment as the input value of CARIMA model, to the predicted value of change of blood sugar into
Row updates；
S3.2: the input according to the predicted value of updated change of blood sugar as LMS control model updates insulin pump
Control input increment；Wherein, LMS control is the adaptive softening factor, the adaptive softening using the softening factor
The factor can adjust the size of own value according to the variation of blood glucose value；
S3.3: circulation executes S3.1~S3.2, realizes that the control input increment to insulin pump optimizes.
9. according to the generalized predictive control infusion of insulin amount calculation method described in claim 5, which is characterized in that the S3 packet
Include following sub-step:
S3.1: using the control of insulin pump input increment as the input value of CARIMA model, to the predicted value of change of blood sugar into
Row updates；
S3.2: the input according to the predicted value of updated change of blood sugar as LMS control model updates insulin pump
Control input increment；Wherein, LMS control is the adaptive softening factor, the adaptive softening using the softening factor
The factor can adjust the size of own value according to the variation of blood glucose value；
S3.3: circulation executes S3.1~S3.2, realizes that the control input increment to insulin pump optimizes.
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| CN202010351347.6A CN111643771B (en) | 2019-04-30 | 2020-04-28 | Closed-loop insulin infusion system based on adaptive generalized predictive control |
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| CN112535777A (en) * | 2020-11-27 | 2021-03-23 | 江苏省苏北人民医院 | CGM (China general microbiological culture collection) -based critical patient intelligent blood glucose management system |
| CN114300091A (en) * | 2021-12-07 | 2022-04-08 | 姜京池 | Self-adaptive adjustment method and device for insulin infusion scheme and storage medium |
| CN116504355A (en) * | 2023-04-27 | 2023-07-28 | 广东食品药品职业学院 | Closed-loop insulin infusion control method, device and storage medium based on neural network |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112535777A (en) * | 2020-11-27 | 2021-03-23 | 江苏省苏北人民医院 | CGM (China general microbiological culture collection) -based critical patient intelligent blood glucose management system |
| CN112535777B (en) * | 2020-11-27 | 2022-12-02 | 江苏省苏北人民医院 | CGM (China general microbiological culture collection) -based critical patient intelligent blood glucose management system |
| CN114300091A (en) * | 2021-12-07 | 2022-04-08 | 姜京池 | Self-adaptive adjustment method and device for insulin infusion scheme and storage medium |
| CN114300091B (en) * | 2021-12-07 | 2022-12-02 | 姜京池 | Self-adaptive adjustment method and device for insulin infusion scheme and storage medium |
| CN116504355A (en) * | 2023-04-27 | 2023-07-28 | 广东食品药品职业学院 | Closed-loop insulin infusion control method, device and storage medium based on neural network |
| CN116504355B (en) * | 2023-04-27 | 2024-04-02 | 广东食品药品职业学院 | Closed-loop insulin infusion control method, device and storage medium based on neural network |
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