# Research on Real-Time Monitoring and Performance Optimization of Suspension System in Maglev Train
## 6 Experimental Verification and Analysis
### Observer and Performance Optimization  
Collecting input and output data during normal operation of the system and constructing an observer-based residual generator according to [32]. \[\left\{ \begin{array}{l}x_{o}(k+1)=\left[\begin{array}{ccc}0&0&0\\ 0&1&0\\ 0&0&1\end{array}\right]x_{o}(k)+\left[\begin{array}{c}0.0009\\ -0.1005\\ -0.0019\end{array}\right]u(k)+\left[\begin{array}{c}-0.5098\\ 0.4688\\ 0.5200\end{array}\right]y(k)\\ \\ r(k)=0.4790y(k)-\left[\begin{array}{cc}0&0&1\end{array}\right]x_{o}(k)-0.1016 u(k)\end{array} \right. \tag{29}\]  
The residual signals of the single suspension system during the operation are shown in Figure 6. It can be found that during the operation, the fluctuation of the residual of the system is very small, most of which are between \(-0.1\;\mathrm{mm}\;\sim 0.1\;\mathrm{mm}\). The mean value of the residual is 0 and the residual approximately obeys the normal distribution. In addition, when the system is disturbed by an unknown operation from the outside, the residual fluctuates between \(-0.3\;\mathrm{mm}\;\sim 0.3\;\mathrm{mm}\), and the average value is almost unchanged. Since the suspension gap of the train fluctuates little, it is still in safe operation. Therefore, the small disturbance during the operation will not have a major impact on the performance of the system, and the system is in a state of no degradation at this time. Setting \(R_{th1}=max\{|r_{max}|,|r_{min}|\}\) and \(R_{th2}=2R_{th1}\), and the control performance of the system can be distinguished by the logic in Section 4.3. During the operation of the train, passengers constantly get on and off the train, and the situation of getting on and off the passengers is unpredictable, which might cause the load to fluctuate in a large range. In order to verify the perfo  
Figure 5: Gap during normal operation. residual generator in the case of large load operation and large load fluctuations, from the 10 th second, loads of different sizes are applied to the electromagnet to simulate passengers getting on and off the train. Residual and gap are shown in Figure 7.  
It can be found that when the load changes, the residual and the gap change at the same time. The amplitude of the residual sharply increases. The maximum value of residual is \(4.78\) mm,which is much larger than the threshold \(R_{th2}\); the minimum value of residual is \(-4.78\) mm,which is much smaller than the threshold \(-R_{th2}\). Therefore, by analyzing the residual signal, real-time monitoring of the large fluctuation of the load of the system can be carried out. The gap and residual when the performance is experiencing degradation and when the train is in normal operation are shown in Figures 8 and 9, respectively. When the suspension system is degraded, the change in the suspension gap is not obvious, and the degradation of the suspension system cannot be judged by analyzing the gap. However, the variance of the residual signal in the two cases is different. When the system is degraded, the fluctuation range of the residual is \(-0.59\) mm \(\sim 0.81\) mm,which is larger than that of the normal operation. Therefore, performance degradation of the suspension system can be judged by observing the residual signal. Figure 6: The residual during the operation. Figure 7: Gap (**a**) and residual (**b**) when the load fluctuates. For real-time performance optimization under performance degradation, the initial values are set as \(\theta_{AB,r}=0\), \(\theta_{C,r}=0\), and \(\theta_{D,r}=0\). During the entire performance optimization period, \(\theta_{r}\) is continuously updated iteratively until the end time of the algorithm has been reached. Finally, \(Q_{C}(z)\) is:  
\[\left\{ \begin{array}{l}x_{r}(k+1)=r(k)\\ u_{r}(k)=0.0657x_{r}(k)+0.0099r(k)\end{array} \right. \tag{30}\]  
The gap and residual of the four periods when the system is disturbed by the disturbance during the performance optimization are shown in Figures 10 and 11, respectively, where \(t_{4}\) is the time before the algorithm terminates.