Artificial pancreas self-adaption and active-disturbance-rejection controller based on blood glucose variation trend

Abstract

The invention provides an artificial pancreas self-adaption and active-disturbance-rejection controller based on a blood glucose variation trend. The controller comprises a tracking differentiator module, an extended state observer module, a non-linear feedback module and a constrained module. A non-linear feedback model of the non-linear feedback module is u=(-fhan(k1e1, k2e2, r2, a)-z3) / b0, wherein k1, k2 and a are blood glucose variation trend self-adaption parameters, r2 is named as controlled quantity gain, e1 and e2 are a signal of an error between a blood glucose concentration set valueand a blood glucose concentration estimated value and a signal of an error between a blood glucose concentration variation rate set value and a blood glucose concentration variation rate estimated value respectively, z3 is a total disturbance estimated value, and b0 is a known gain coefficient. According to the controller, all uncertain factor actions on a controlled object come down to unknown disturbances, and the disturbances are estimated by using input and output data of the object and compensated. Therefore, the controller algorithm has certain robustness to the disturbances, such as personal parameter inaccuracy, model uncertainty, eating disturbance and before-meal dose inaccuracy.

Description

technical field [0001] The invention relates to an artificial pancreas self-adaptive active disturbance rejection controller based on blood sugar variation trend, and belongs to the technical field of artificial pancreas systems. Background technique [0002] Blood glucose concentration in normal healthy people is regulated by insulin and glucagon. Glucagon is secreted by alpha cells in the pancreas and increases blood sugar levels, while insulin is secreted by beta cells in the pancreas and lowers blood sugar levels. When β cells lose function or die due to autoimmune damage, and insulin is not secreted at all, this symptom is type 1 diabetes, and when β cells cannot produce sufficient insulin, this symptom is type 2 diabetes. Both type I and type II diabetes are metabolic diseases characterized by hyperglycemia, and can lead to serious long-term complications such as cardiovascular disease, chronic kidney disease, diabetic foot, retinopathy, etc. Collectively referred to...

AI Technical Summary

Problems solved by technology

[0005] 2) Multiple types of interference

The rise in blood sugar caused by digestion and absorption after ingestion is a fast-response kinetic process, while the absorption and action of subcutaneously injected insulin to lower blood sugar is a slow kinetic process, and the blood glucose sensor passes through the subcutaneous intercellular fluid and There is also a certain time lag and inaccuracy in the measurement of blood glucose concentration by the concentration difference of blood glucose in blood vessels

[0007] 4) Asymmetric risk of hyperglycemia and hypoglycemia

As an industrially practical control algorithm, PID is also the first to be used in the artificial pancreas system. It uses the proportion, integral, and differential weighted sum of the error between the blood sugar concentration target value and the actual value to form the feedback control amount. It has a simple structure. It is easy to implement, but it is also limited by the simple structure. The characteristics of the blood glucose metabolism system, including the above-mentioned problems and challenges that need to be considered, cannot be reflected in the controller design. There is a phenomenon of integral saturation, which is likely to cause excessive insulin injection when the blood sugar continues to rise.

MPC can directly deal with the control problems under various constraints, and has certain robustness to the system model. By predicting the future output and finding the optimal or suboptimal control amount, it can easily deal with food intake, insulin absorption delay, etc. , has the characteristics of superior performance, but the complexity of the algorithm is relatively high, and the operation of the controller takes up more computing resources, which is not conducive to the reliable implementation in embedded systems

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