# Model Predictive Control and Its Role in Biomedical Therapeutic Automation: A Brief Review
## 3 MPC in Biomedical Applications
### Type-1 Diabetes  
The blood glucose level (BGL) of a healthy person fluctuates within a healthy range of 70-110 mg/dL. The glucose-regulating process in the body is comprised of two regulating inputs, glucagon and insulin, which are used to control blood glucose output into the blood stream. BGL is reduced by insulin and increased by glucagon. Type 2 diabetes (T2D) and type 1 diabetes (T1D) are both caused by the failure of the pancreas to release insulin, either partially or completely. T1D is the most frequent of the four types of diabetes, and it is primarily caused by pancreatic dysfunction [63]. Long-term anxiety and stress, as well as a lack of physical activity, contribute to the development of type 1 diabetes. In these patients, long-term hyperglycaemia (deviation from the normal BGL to a higher value) leads to multiple complications, such as cardiovascular disease, kidney failure, neuropathy, and retinopathy. On the other hand, hypoglycaemia (deviation from the normal BGL to a lower value) causes complications such as tachycardia and nausea. Even coma (at BGL of 50-70 mg/dL) is a likely complication of disrupted BGL regulation [64; 65; 66]. Insulin injections (subcutaneous, intravenous, or intraperitoneal) are recommended as common therapy for pancreatic cell death in non-automated clinical procedures [65]. BGL regulation is a tough control task in T1D patients, complicated by severe nonlinearities, complex dynamics and unexpected disruptions. Control algorithms for blood glucose control in type 1 diabetes patients should be capable of successfully dealing with fluctuations in everyday living. Furthermore, careful consideration must be given to the design of the controller in order to deliver insulin safely and avoid hypoglycemia. Hypoglycemia can arise as a result of fasting or excessive physical exercise. Overeating or being exposed to stressful situations contribute towards hyperglycemia. In general, physicians determine exogenous insulin dosage for a patient through an experimental procedure based on measured blood glucose levels. This procedure can be automated by designing a closed-loop automatic BGL control framework that determines the amount of applicable injectable insulin, taking into account various T1D condition parameters. Several researchers haveattempted to create dynamic models characterising the human blood glucose relationship in recent decades. [PERSON] et al. [66] presented an overview of the major glucose-insulin models developed for type 1 diabetes. The well-known Bergman minimal model (BMM) has been reported to be the most preferred among BGL control researchers [67; 68]. There are many options for dealing with type 1 diabetes when it comes to control methods. Herein, proportional-integral-derivative (PID) control is the most prevalent traditional control system. For BGL regulation, intelligent techniques such as artificial neural networks and metaheuristic algorithms [69] are frequently used. The backstepping technique and robust H control [70; 71] have also been used in some studies [72; 73]. Among all blood glucose control approaches, MPC is one of the most well-known and widely applied methods. MPC is an effective control algorithm for BGL regulation since it can readily combine the dynamics of glucose-insulin, meal information and insulin injection constraints. MPC is one of the most suited approaches for designing control systems that are subjected to a variety of restrictions, since constraints are intrinsically incorporated into the control design procedure [74]. Several studies have described MPC-based blood glucose regulation using both linear [75] and nonlinear techniques [76]. The nonlinear model predictive control (NMPC) technique is one of the most effective methods of dealing with disturbances in the modelling of a T1D patient and reaching/maintaining the ideal blood glucose level. An appropriate insulin pattern is generated using the NMPC algorithm in conjunction with essential injection restrictions as well as hyperglycemia and hypoglycemia constraints.