## 1 Introduction
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**References** & **Proposed Control Methods** & **Results/Findings/Limitations** \\ \hline [PERSON], [PERSON] [5] (2000) & Fuzzy logic control (FLC) & Regarding the open loop, passenger comfort for small/medium road disturbances was improved by 58\%, while the maximum bump amplitude for which the suspension limits cannot be reached was increased by 18\%. \\ \hline [PERSON] [6] (2000) & FLC & Both body ride and suspension workspace responses exhibited good damping characteristics for different road inputs. \\ \hline [PERSON] et al. [7] (2002) & Sliding mode neural network inference fuzzy logic control (SMNNNFLC) & The simulation results showed that the proposed SMNNNFLC provided higher ride comfort and handling quality than the other methods presented in the paper. \\ \hline [PERSON] et al. [8] (2004) & Proportional-integral sliding mode control & The method was shown to be more robust than the benchmarked method. \\ \hline [PERSON] and [PERSON] [9] (2005) & Constrained Ho control & As a result of this study, when the suspension strokes and control inputs were kept within the defined limits, tight contact of the wheels with the road was ensured and the best possible ride comfort was achieved. \\ \hline [PERSON] et al. [11] (2008) & Fuzzy sliding-mode control (fuzzy-SMC) & It was seen that the magnitudes of the body displacement and pitching motion were reduced and the resonance peak caused by the vehicle body was eliminated. It was also seen that the magnitudes of the body and pitching motion acceleration were reduced in a wide frequency range, thus providing a significant improvement in driving comfort. \\ \hline [PERSON] and [PERSON] [13] (2009) & FLC & Improved driving comfort by reducing body acceleration caused by the vehicle body in road disturbances \\ \hline [PERSON] and [PERSON] [14] (2010) & Hybrid self-organizing fuzzy and radial basis function neural network controller (HSFRBNC) & The challenge of finding suitable parameters for SOFC design were eliminated. It was shown to improve the service life of the suspension claim and the ride comfort of a car. \\ \hline [PERSON] et al. [25] (2016) & Adaptive neural network control & In the simulation results, the vehicle body vibrations were effectively suppressed, and RMS values were improved. \\ \hline [PERSON] and [PERSON] [22] (2016) & FLC & Positive results were obtained in terms of applicability in ASSs. \\ \hline [PERSON] et al. [25] (2017) & Fuzzy control with dynamic sliding mode approach & It was observed that the spring-mass acceleration under road disturbance was significantly reduced, and lower body acceleration was achieved. \\ \hline [PERSON] and [PERSON] [36] (2022) & Adaptive harmonic control & Using the proposed method, better results were obtained in some performance criteria compared to other control algorithms. \\ \hline  & & In the case of the bump road profile, the percentage reduction in the RMS value of the body acceleration was 64.6\% for the proposed BA-tuned LQR and 37.5\% for the GWO-tuned LQR. \\ [PERSON] et al. [38] (2022) & BAT/GWO-LQR & Similarly, for the pulse path profile, the reduction in the RMS value of the body acceleration compared to the conventional LQR was 43\% for the BA-tuned LQR and 25.2\% for the GWO-tuned LQR. \\ \hline \end{tabular}
\end{table} 
Table 1: The comparison table of the existing studies. * Using the passive state of the ASS, LQR control method, PSO-based fuzzy logic control (FLC), and PSO-based fuzzy-LQR control methods together, and comparing the obtained results is the second important contribution of this study. * Comparing the methods used with each other using the ITAE performance index and comparing the method showing the most successful performance among the methods with the studies in the literature is another contribution of the study.