# Adaptive Semi-Active Suspension and Cruise Control through LPV Technique
## 5 Simulation Results  
A compact utility truck is selected for the simulation with independent rear and the front suspension and half a tonne of payload. Parameters of the simulated truck are shown in Table 4. The simulation route is a highway based on real geographical and speed limit data, see Figure 8a and integrated to the TruckSim environment, see Figure 8b. In order to demonstrate the effectiveness of the proposed method two different simulations have been performed and compared, one with the utility truck having conventional semi-active suspension and another with an adaptive semi-active suspension control. Note, that only a 3 km section of the route has been considered, with the road geometry shown in Figure 9b. The figures in this section have been plotted with the travel distance for the simulated vehicle and its performance results that were provided by Trucksim. As it is shown in Figure 9b, there are three major curves in the selected road section around 400 m, \begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **Value** & **Unit** \\ \hline Truck mass (\(m_{t}\)) & 760 & kg \\ Payload mass (\(m_{p}\)) & 500 & kg \\ Distance from C.G to front axle (\(l_{1}\)) & 0.55 & m \\ Distance from C.G to rear axle (\(l_{2}\)) & 1.375 & m \\ Track width (\(b\)) & 1.26 & m \\ Height of COG (\(h_{COG}\)) & 0.813 & m \\ Maximal suspension deflection (\(d_{max}\)) & 70 & mm \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Vehicle parameters. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Road Type** & **Location (m)** & **Selected \(\rho\)** \\ \hline Multiple bumps & 200 & 0.52 \\
10 cm bump & 950 & 0.6 \\
10 cm bump & 2000 & 0.54 \\ Multiple bumps & 2200 & 0.47 \\ Sine-sweep & 2650 & 0.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Selected scheduling variable. Figure 7: Tire deformation map for 10 cm bump. 1000 m and 1700 m. Moreover, there are also three different kinds of road irregularities to simulate different road distortion, see Figure 10. First, in 2 different locations in the road 200 m and 2200 m there are bumps and potholes following each other differently for the left to the right side of the lane, having a height and depth of 50 mm. This road type is a kind of road irregularity that represents the effect of driving over a speed bump, which is common in urban areas. Next, after in 2 different locations around 950 m and 2000 m, there are left and right side bumps following each other having 100 mm of height. This road irregularity represents bad road quality with discontinuities in the asphalt. Finally, at 2650 m there is a longitudinal sinusoidal road irregularity with growing frequency, putting extreme stress on the suspension system of the simulated vehicle. This kind of road surface is typical at bus stops, where the frequent braking of heavy road vehicles roll up the asphalt. As shown in Figure 11, the real speed limit changes on the route, and both vehicles calculate its corresponding optimal velocity based on the energy-efficient look-ahead method. According to road distortions and velocity provided by the look-ahead controller, the decision layer selects a different value for the scheduling variable. Figure 8: (**a**) Real data simulation route. (**b**) TruckSim environment. Figure 9: (**a**) X-Y geometry of the road section. (**b**) Station-Z geometry of the road section. Figure 12 shows selected scheduling variable, where selection process was mentioned in Section 4 for the adaptive control simulation and constant scheduling variable for comparison. Selected \(\rho\cong 0.52\) for the first several bumps at 200 m, \(\rho\cong 0.6\) for the 10 cm bump at 950 m, \(\rho\cong 0.54\) for the second 10 cm bump at 2000 m, \(\rho\cong 0.47\) for second several bumps which is at 2200 m and for the sine sweep which is at 2650 m, \(\rho\cong 0.5\). The performances are compared for the two simulated vehicles. In Figure 13 it can be seen that vertical acceleration performance has been improved both in flat road and at the road distortions.