## 7 Discussions and Conclusions  
This study investigated some contemporary ML-MR adaptive control structures that are applicable for classical PID control loops. These structures are the ML-MR PID-MIT, ML-MR PID-MIT-CGM, ML-MR PID-I, ML-MR PID-IM, ML-MR MIT-PID-PFA, and ML-MR PID-PID-PFA control structures. Design problems of these ML-MR structures were addressed, and straightforward design and implementation strategies were presented. The performance evaluations, advantages, and drawbacks of these ML-MR structures were discussed according to control simulation results. Simulation results indicated significant disturbance rejection performance improvements in four different control application examples. The performance comparisons with several single-loop optimal control loops theoretically show that improvement of the disturbance rejection control without affecting the set-point performance is possible with presented ML-MR control structures. Some noteworthy properties of the presented ML-MR adaptive control structures are listed in Table 2.  
\begin{table} \begin{tabular}{c c c c c c} \hline \hline
**ML-MR Control** & \multirow{2}{*}{**Character**} & \multirow{2}{*}{**Dependence to**} & **Negative Effects** & \multirow{2}{*}{**Applicability for Time**} & **Indications in** \\
**Structures** & & **Set-Point** & & **on Set-Point** & **Daly Systems** & **Simulation** \\  & & **Level** & **Performance** & & **Results** \\ \hline ML-MR PID-MIT & Nonlinear & Dependent & None & None & Figures 3 and 4 \\ ML-MR & Nonlinear & Dependent & None & None & Figure 8 \\ PID-MIT-CGM & Linear & Independent & None & None & Figure 10 \\ ML-MR PID-IM & Linear & Independent & None & None & Figure 14 \\ ML-MR & Nonlinear & Dependent & None & Applicable to some degree & Figure 17 \\ MIT-PID-PFA & & & & & \\ \hline \hline \end{tabular}  Some notable remarks: * ML-MR adaptive control structures are applicable in all classical control loops in order to improve the disturbance rejection performance without modifying the parameters of the existing control system or degrading the set-point performance. This is an important advantage of hierarchical ML-MR structures in control engineering * Integration approaches of the model reference control loop into classical control loops are carried out in two steps: outer loop integration uses closed loop control systems as reference models, such as ML-MR PID-MIT, ML-MR PID-IM, and ML-MR PID-IM controls; and inner loop integration uses models of plant or process functions as reference models, such as ML-MR MIT-PID-PFA and ML-MR PID-PID-PFA. Outer loop integrations cover the whole closed control loop to deal with disturbances, and several works have indicated that these type integrations are preferable solutions for improving fault tolerance and disturbance rejection in the whole control loop [25; 26; 27; 28]. Inner loop integration only encloses the plant function, and hence it can be employed to deal with external disturbances or parametric perturbations that influence the controlled system. * ML-MR PID-MIT-CGM and ML-MR PID-I structures can adaptively increase the RDR performance of the closed control loops without affecting set-point performance. The ML-MR PID-IM control structure benefits from having an internal model controller design, however it should be noted that its performance is very sensitive to the accuracy of plant and controller models. * Large-time delay systems that set the point and disturbance rejection performance of ML-MR PID-PID-PFA control structures can surpass the disturbance rejection performance of a single-loop optimal FOPID control system. These results reveal that the disturbance rejection capacity of closed loop PID control loops can be exceeded by combining them with ML-MR control structures. ML-MR control structures are a feasible way to resolve performance tradeoff between set-point and disturbance rejection control structures.