## 5 Proportional Integral Derivative (PID) Controller
It is the classic PID controller which is based upon the CAD methodology having a dynamic model of the control system. CAD is based upon frequency response (FR) tool [124]. An expert PID controller is designed to regulate the BG levels. It uses clinical sliding table technique. The sliding table contains multiple insulin concentration rates. The sliding table implements the proportional control scheme and this table is improved with the condition of the intended patient [125]. The proportional integral (PI) controller is the most common used controller in it. The P and I controller are implemented for the individual purposes. There are many methods to tune the PI controller, one of those is trial and error method. In this method, the gains of the proportional and integral were set randomly to boost the performance of insulin delivery system. Controller designers improved the steady and transient achievement of the PI controller by introducing fuzzy theory [108]. [PERSON] et al. [126] explained a PID controller strategy which is evaluated in silico using the physiological Hovorka model. There are some key points of the strategy: (i) switching strategy for the PID initiating, (ii) a novel time varying set point trajectory, (iii) reducing the sensor noise and noise derivative with a filter, and (iv) a strategy is used to tune the controller. The proposed PID strategy is widely used in  
Figure 8: Conceptual overview of the proportional integral derivative (PID) controller used in T1 DM treatment and control. the industrial applications. The PID controller is best because it mimics the first and second phase responses. The controller is based upon novel PID controller. The authors explained various control strategies, which are: (i) bolus only (ii) PID control only (iii) bolus plus PID control (iv) bolus plus PID control with switching criteria, and (v) bolus plus PID control with switching criteria and time varying glucose set point which are called improved PID (IPID). [PERSON] et al. [127] discussed a fuzzy logic-based controller for glucose regulation in the T1 DM patients. To get rid of daily injection insulin and to obtain professional education about the treatment of the disease, Mamdani type fuzzy logic controller is simulated. [PERSON] et al. [128] discussed an IPID controller and explained some new features of it. The controller is on \"silico\" using the physiologic model of Hovoraka which is the best glucose-insulin dynamics model. The key features of the proposed control strategies are (i) switching strategy, (ii) novel time-varying set point (iii) noise and derivate filters, and (iv) systematic controller tuning strategy. The IPID controller of this type are built upon the novel PID controller and bolus injection for the meal [128]. [PERSON] et al. [129] described a glucose insulin system model with only few parameters. The model is designed using Mamdani type fuzzy structure. It has two input and one output variables. The inputs are error and its rate, and output is rate of the insulin infusion. To make the mathematical model, the Stolwijk-Hardy glucose-insulin interaction model is employed. The exogenous insulin infusion term is added in the modified model to yield superior results.