## 10 Conclusions and Future Work  
In this review article, we have described the fundamental components of the artificial pancreas (AP) system used for type 1 diabetes mellitus (T1 DM) treatment and control. We have explained the different methods of administration and delivery of insulin in T1 DM patients, glucose measurement (GM), and the most widely used control algorithms. In addition, we explained the strengths and weakness of the available control strategies used for diabetes treatment and control. We have discussed many techniques related to the insulin infusion in human body. The open loop, closed loop, linear and non-linear schemes, and various controllers have been explained in detail to enable research beginners to understand this domain effectively. The Simulink models has been formally explained and plotted with the mathematical formalization to explain their backend functionality. The control loop strategy has been widely used in AP systems and works like a real human pancreas system. We described three prominent control algorithms such as PID, MPC, and FLC in detail, which are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Controller** & **Advantages** & **Disadvantages** & **References** \\ \hline Self-Tuning & Accurate physiological response with short time. & Parameter optimization and manual tuning is required. & [134] \\ \hline Sliding Mode Control & Better robustness and insensitivity to the inter-patient variability/diversity in metabolic conditions. & SMC has some intrinsic problems such as discontinuous control that suffers from the chattering. SMC is only applicable for the degree one systems otherwise higher order sliding mode ( HOSM) is used. & [137; 138] \\ \hline Adaptive Control & It can react promptly during the large and rapid variations in insulin action. & In the presence/entrance of the unknown parameters in the process model, it becomes relatively difficult to construct a continuously parameterized controller. & [118; 119] \\ \hline Model Predictive Control & It can be tuned for personalized insulin delivery. It has feed-forward insulin action for delayed insulin effect. & There is no compensation for the unknown disturbances, and the metabolic uncertainty is not considered in the MPC. & [122] \\ \hline Hoo & Hoe controller works well in the presence of uncertain parameters. & Unable to effectively resolve the tradeoff between the strength of control action and the tracking error. & [123] \\ \hline SDRE & Can tackle any non-linear terms, and effectively maintains the non-linear characteristics of the system. It exhibits robustness against parametric uncertainties. & It involves very complex mathematical calculations especially when a system is of higher order. & [124] \\ \hline PID & It is the best controller with situation awareness. It may be proportional (P), proportional-integrative(PI), proportional derivative (PD), and/or PID. & Its physical implementation for the clinical trials is relatively difficult. & [105] \\ \hline Fuzzy logic & Fuzzy logic is opposite to that of binary logic. It is helpful at any point between 0 to 10. & Simulations wise developed but its validity in clinics has not been rigorously verified. & [116] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the different controllers used for T1 DM assessment and control. proven clinically, and they yield promising results. In future work, we intend to explore more advance control techniques used in the AP systems, and their structure and challenges. In addition, we aim to present a comprehensive review about the recent developments in the type 2 diabetes mellitus (T2 DM) treatment and control. Furthermore, the current coronavirus disease 2019 (COVID-19) pandemic has forced clinicians to reconsider the ways in which effective diabetes management is delivered during these challenging times around the globe [214; 215].