## 5 Conclusions and Future Works  
The above study suggests an energy-efficient extremum seeking algorithm without steady-state oscillations embedded in the prescribed performance control technique in a vehicle platoon system to minimize the aerodynamic drag force between consecutive vehicles and help the system safely maintain a desired formation while avoiding connectivity issues. Simulation results are quite promising since the goal is achieved and there is no need for a high-end hardware setup in the first place. The algorithm indeed seems to optimize the system in terms of energy demands. However, we should take into consideration that several constraints have been set for the system, and in a real environment, several complex conditions take part in energy consumption. To ensure experimental feasibility, various tests should be conducted to investigate potential solutions for various problems that are caused by uncertainties in the model. Additionally, drag behaviour should be examined under different weather/road conditions to extract richer data and provide a more realistic approach to the drag profiles. Reassured that the algorithm can work under the aforementioned issues, the next immediate goal is to conduct platoon experiments to verify its actual potential. One main objective is to find out whether the oscillations in the \begin{table}
\begin{tabular}{c c c} \hline  & **Energy [G]** & **Computational Time [s]** \\ \hline SMC with ESCWAO & 0.677 & 5.61 \\ \hline PPC with ESCWAO & 0.586 & 12.17 \\ \hline \end{tabular}
\end{table} 
Table 1: The energy demands and computational time comparison for the two different implementations. Figure 8: Required energy that each vehicle and the platoon system demand in total after the simulation, under the three different control cases. transient state, as shown before in Figure 7, can highly affect driving comfort or not. On the other side, the last statement shows that this work can immediately contribute to studies for unmanned vehicles, where comfort issues are not taken into consideration. Further research can explore how the algorithm can be implemented in real-world transportation systems to improve performance and fuel efficiency. Additionally, research can focus on refining the algorithm to accommodate for uncertainties, such as wind tunnel calibration errors and sensor inaccuracies, that may affect the experimental observations of drag coefficients. In this work, only the longitudinal problem is considered and the vehicle state is obtained directly, which is impossible in a real scenario. Future research can focus on the case. Adopting methods using Kalman filters is a common solution to these problems. For instance, in [23], Kalman filter techniques are proposed to estimate the velocity errors and improve the observations of the heading vector in a complex autonomous vehicle dynamic model. Another interesting study [24] proposes Kalman filter techniques to estimate roll and pitch for vehicles in a platoon system using only the planar kinematic model. These robust works in total rely on the estimated speed, position, and orientation of the vehicles. Another common issue in the real application is the system delays due to different modules which are not considered in our control design. In [25; 26], different solutions are proposed to solve the issue for a variety of sensors, both in unmanned independent systems and platoon formations. Adapting the insights of these works in our proposed algorithm can open the way to utilize it in a real platoon scenario. Lastly, a significant challenge is to develop an ESC scheme that considers more than one parameter to improve the accuracy of the optimization, such as energy consumption based on the engine torque. Finally, research can also delve into how to optimize platooning systems considering different weather conditions, altitudes, and other environmental variables that are not considered in this work. Conceptualization, C.P.B. ; methodology, A.K. and C.P.B. ; software, A.K. ; validation, A.K. ; formal analysis, A.K.