## 4 Real-Time Implementation and Results  
In this section, the experimental results for the proposed multi-controller and multi-model approaches with the MAGLEV system are presented. The local-platform system is proposed, as shown in Figure 11. LabVIEW was used to implement the real-time part with NI USB-6008 12-bit as the data acquisition. The MAGLEV system hardware also contains a Hall effect 49E sensor, electromagnetic coils, filters (hardware), a Dc Driver, and a real-time operating environment. As an experiment, Case 1 was imposed, and the model M2 was the model of the system. Therefore, the controller of M2 in the multi-control approach was C2 \((\mathbf{R_{2}},\mathbf{S_{2}},\mathbf{T_{2}})\) but the system was forced to choose controller C1 as a test to see the response and performance of the Maglev system. Figure 12 depicts the response and performance of Case 1; slow rise time, high overshoot, high settling time, and weak rejecting the disturbances. Figure 10: Adaptive Supervisor and switching. In the same way, considering the M2 as the main model of the system (Case 2), the process was repeated, but this time with \((R_{3},S_{3},T_{3})\) as the controller. The result was also not good, with slow rise time, higher overshoot, higher settling time, and weak ability to reject the disturbances, as shown in Figure 13.  
Figure 14 depicts the effect of multi-model and multi-control on the MAGLEV response; the three zones show good and strong performances as overshoot, rise time, settling time, and rejection of the disturbances for all zones. Table 1 shows a comprehensive comparison using the basic criteria. Figure 11: The magnetic levitation system platform. Figure 12: Performances for Z2 operating point with \((R_{1},S_{1},T_{1})\) algorithm *. Figure 13: Performances for Z2 operating point with \((R_{3},S_{3},T_{3})\) algorithm *. The proposed real-time platform presents an economical solution that is not expensive (hardware and software) and more stable (hardware and software from National Instruments Company [23]) compared with our previous paper [13], which used elements from different companies. For example, the DC Driver and filters were created from cheap and basic components (resistors, capacitors, transistors, etc.). The test of switching showed in Figure 15 that this method is stable. We can see that in both tests, there are either no shocks or low bumps in how the control evolves. As shown in Table 2, all of the parameters (overshoot, settling time, rise time, and shocks test) are excellent and proof that each model/identified process, in a closed loop, is stable. If there were more (4 or 5) models or algorithms, the little oscillations could be stopped. \begin{table}
\begin{tabular}{c c c c c} \hline
**Controller Types** & **Overshoot** & **Settling Time** & **Rise Time** & **Oscillations** \\ \hline Switching from Z1 to Z2 & 7.78\% & 0.94 & 0.32 & Very small \\ Switching from Z2 to Z3 & 1.1\% & 0.81 & 0.312 & No \\ \hline \end{tabular}
\end{table} 
Table 2: Switching test in closed loop for all the zones. Figure 14: Multi-model controller real-time with Maglev system application and performances for Z2 operating point with \((R_{2},S_{2},T_{2})\) algorithm *. Figure 15: Switching test (Z2 to Z3). \begin{table}
\begin{tabular}{c c c c c} \hline
**Controller Types** & **Overshoot** & **Settling Time** & **Rise Time** & **Disturbances** \\ \hline RST\({}_{1}\) with M2 & 8.33\% & 1.1 & 0.36 & Not good \\ RST\({}_{3}\) with M2 & 16.4\% & 1.4 & 0.373 & Not good \\ Multi (Model, Controller) & 1.1\% & 0.81 & 0.312 & Good \\ (proposed solution) & & & & \\ \hline \end{tabular}
\end{table} 
Table 1: Shows a comparison of the proposed solution (multi-model and multi-controller) (Zone 2 as a case study) versus a single controller (C1 with M2 and C3 with M2). The last comparison is between the results obtained in our previous paper [13] under the title (design of PID controller for nonlinear magnetic levitation system using fuzzy-tuning approach) (Figure 16) and the method in this paper.