## 9 Lesson Learned and Discussion  
In this review paper, we have summarized the insulin administration, glucose measurements, and control strategies used in artificial pancreas (AP) systems. A large number of control algorithms have been proposed for the AP. Among these, only MPC, PID, and FLC have been used experimentally in clinical trials. The PID controller can be personalized or made patient-specific by tuning its parameters accordingly. In MPC strategy, the weighting function of the objective function or model is used for the individual predictions [192], whereas the adaptive controllers have been used in conjunction with other controllers such as PID and FLC to provide real-time adjustment of the parameters [193]. Meanwhile, it is relatively challenging to decide as to which control strategy is appropriate for desirable performance. Furthermore, due to the diverse factors and hidden complexities, the development of controllers based on personalized mathematical models using the CGM data, and devising computational models, mainly based on machine-learning methods, that reproduce the CGM data/values and controllers based on such data-based models, are very challenging. Meanwhile, the full automation of insulin regulation, and the solution of the underlying closed-loop control problems, are the main elements in the success of the T1 DM's treatment based on the AP paradigm. Furthermore, most of the controllers discussed in the study lack implementation in clinical trials. A synthesis of the controllers addressing the large diversity of metabolic factors has not been devised. With the significant advancements in machine-learning methods, determining the accurate medications highly suitable for treating different comorbidities of the diabetic has become reality [194]. Training of the machine-learning models on the sufficiently large datasets can helps in suggesting the most appropriate medications for T1 DM patients. Apart from this, the scheme which tunes the insulin is based on broad limits and proportional gain. Such schemes provide robust performance considering circadian variations, mixed meals, and the common problems of the disturbance rejections and sensor errors. The invention of the low-cost sensor data-processing technologies in a CGM, and data-driven adaptive controllers that can auto-correct errors and change the insulin infusion rates can revolutionize the T1 DM control. Until now, there have been three major eras which show the improvements of the control strategies in the AP systems [195]: (i) first era covers the closed-loop control algorithms reported between 1963-1981, (ii) second era includes the model-based control algorithms developed from the 1900s to the present, and (iii) the third era includes current control algorithms based on data. In the first era, the control problem heavily depends upon the glycemic target defined by the physician, and the controller is an algorithm able to compute the insulin amount to be infused by the CSII system such that the goal of the treatment could be achieved. In the second era, the new challenge was to develop the control algorithms based on the physiological information of glucose metabolism and the control signal should have physiological meaning. It establishes a new paradigm encouraged by two research problems/areas: the mathematical modeling of glucose metabolism, and the synthesis of model-based control algorithms. In the third era, the paradigm of the AP, its three elements (CGM system, CSII system, and the control algorithm) have evolved considerably. Recently, many clinical trials have proved the usefulness of the AP as an outpatient therapy. Despite the significant developments, the specific model based on the personalization scheme that can adopt with the specific features of each patient is missing. In addition, the multi-criteria such as considering circadian variations, mixed meals, and the common problems of the disturbance rejections and sensor errors need significant developments [196].