## 5 Conclusions  
In this paper, the model of the suspension system of a quarter-car is established, and an active control system is implemented with three different control methods. In developing the controller, the goal of improving the car handling performance and passenger comfort was achieved, as shown in the graphs. Active control was performed using the ASS, LQR, fuzzy logic control (FLC), and fuzzy-LQR control algorithms. To enhance the performance of the FLC and fuzzy-LQR control methods, the optimal values of the coefficients of the points where the feet of the membership functions touch were calculated using the particle swarm optimization (PSO) algorithm. Then, the designed controllers were simulated in the computer environment. The success of the control performance of the applied methods concerning the passive suspension system was compared in percentages. The methods were compared against one another and the research in the literature using the integral time-weighted absolute error (ITAE) criterion. As a result, it was found that the proposed control method (fuzzy-LQR) is about 84.2% more successful in body motion, 90% in car \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance Criteria (ITAE)** & **PSO-Based Fuzzy-LQR** & **Harmonic [36]** & **H-Infinity [36]** & **MPC [36]** \\ \hline Body travel \((X_{body})\)(m) & 0.00302 & 0.0039 & 0.0071 & 0.0041 \\ \hline Body acceleration \((X_{body})\) (m/s\({}^{2}\)) & 0.07545 & 0.1683 & 0.1588 & 0.1252 \\ \hline Suspension deflection \((X_{body}-X_{shell})\)(m) & 0.00280 & 0.0044 & 0.0072 & 0.0046 \\ \hline Tire deflection \((X_{shell}-X_{read})\)(m) & 0.00015 & - & - & - \\ \hline Actuator \((F)\)(N) & 59.29 & 64 & 95.5 & 80.7 \\ \hline \hline \end{tabular}
\end{table} 
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