# Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID
## 5 Simulation Analysis  
Selected B-grade road surface, with a vehicle speed of 50 km/h, for conducting simulation analysis on the suspension model. The genetic algorithm optimization was utilized to compare the fuzzy PID-controlled active and passive suspensions of the air suspension system before and after optimization. Figure 12: Block diagram of air suspension test bed. Figure 13: Comparison of response curves between simulation results and test results. When the vehicle speed is 50 km/h and subjected to B-class road surface excitation in the suspension model, as observed from Figure 14, the vertical body acceleration is 1.8062 m/s\({}^{2}\). With the application of fuzzy PID control, the maximum acceleration attenuation decreases to 1.3908 m/s\({}^{2}\). Furthermore, employing GA F-PID control further reduces the maximum acceleration to 1.2643 m/s\({}^{2}\). According to Figure 15, the suspension travel is approximately 0.0337 m. With fuzzy PID control, this value decreases to 0.0276 m, and with GA F-PID control, it further decreases to 0.0249 m. Figure 16 indicates that the tire dynamic load is around 6.0895 kN. After applying fuzzy PID control, this load decreases to 5.7241 kN, and with GA F-PID control, it further decreases to 5.5414 kN. From Figures 14-16, it can be observed that the genetic algorithm-optimized fuzzy PID controller significantly reduces the vertical acceleration of the vehicle, suspension wheel travel, and dynamic tire load. After implementing the GA-fuzzy PID control strategy on the air suspension system, the response curves of the entire system become smoother, and the three performance indicators are optimized to some extent. The comfort, smoothness, and operational safety of the vehicle are also enhanced, achieving a relatively ideal control effect. In order to comprehensively validate the effectiveness of the genetic algorithm-optimized fuzzy PID air suspension control, smoothness simulations were conducted in MATLAB under different road grades and at different speeds. The simulation results are shown in Tables 7-9. From Tables 7-9, it can be observed that under the same conditions, compared to passive suspension, both fuzzy PID and GA-fuzzy PID controlled air suspensions show decreased values in suspension performance evaluation indicators, attenuating vehicle body vibrations and enhancing driving smoothness and ride comfort. Taking Grade B road surface as an example, fuzzy PID controlled air suspension, compared to passive air suspension, showed a decrease of 23%, 18%, and 6% in the root mean square values of vehicle vertical acceleration, suspension wheel travel, and dynamic tire load, respectively. Under the same experimental conditions, the evaluation indicator values corresponding to the genetic algorithm decreased by 30%, 26%, and 9%, respe \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Criteria** & **Passive** & **Fuzzy PID** & **GA-Fuzzy PID** & \begin{tabular}{c} **Percentage of** \\ **Fuzzy PID** \\ **Optimization** \\ \end{tabular} & \begin{tabular}{c} **Percentage of** \\ **GA-Fuzzy PID** \\ **Optimization** \\ \end{tabular} \\ \hline \begin{tabular}{c} Vehicle Vertical Acceleration \\ (m/s\({}^{2}\)) \\ \end{tabular} & 1.8062 & 1.3908 & 1.2643 & 23\% & 30\% \\ \hline \
\begin{tabular}{c} Suspension Wheel Travel \\ (m) \\ \end{tabular} & 0.0337 & 0.0276 & 0.0249 & 18\% & 26\% \\ \hline
\begin{tabular}{c} Dynamic Tire Load \\ (kN) \\ \end{tabular} & 6.0895 & 5.7241 & 5.5414 & 6\% & 9\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Test conditions of B-grade road surface at 50 km/h.