## 1 Introduction
The proposed method was experimentally compared with the classical LQR and model predictive control (MPC) methods in three track profiles. The results showed that the proposed method significantly reduced the acceleration of the body due to uneven road profiles compared to the classical tuned LQR and model predictive control (MPC). It was also shown to improve car handling and occupant comfort significantly. [PERSON] and [PERSON] proposed an adaptive fault-tolerant fuzzy control method for active seat suspension systems with full state constraints ([PERSON] and [PERSON], 2019). The method included electromagnetic actuator failures, constraints on the car seat, active suspension and wheel displacement, vertical vibration velocities, and current. In control design, fuzzy logic systems approximate unknown nonlinear dynamics because the systems have dynamic properties such as complexity and spring nonlinearity. A new adaptive fuzzy FTC method used an adaptive recursive backstepping design algorithm with barrier Lyapunov functions. When the electromagnetic actuator failed, the proposed method ensured that all vertical vibration states were stable. [PERSON] et al. (2019) presented a neural algorithm based on reinforced learning to optimize ASSs' linear quadratic controller (LQR). They applied it to the system of a quarter car in a simulation environment. The method was compared with the passive and classical LQR control methods. The method seemed to increase user comfort by 67% compared to the passive system and 14% compared to the non-optimized LQR. Consequently, the comparison of these studies is provided in Table 1 to better comprehend and compare the studies in the literature. In this paper, the suspension model of a quarter-car is created, and active control is implemented. The suspension model is created using the Lagrange-Euler method. LQR, fuzzy logic control (FLC), and fuzzy-LQR control algorithms are developed and applied to the suspension system for active control. The purpose of these controllers is to improve car handling and passenger comfort. Undesirable vibrations occur in passive suspension systems. These vibrations should be reduced using the proposed control methods and a robust system should be developed. The optimal values of the coefficients of the contact points of the membership functions are obtained using the particle swarm optimization (PSO) algorithm to enhance the performance of the FLC and fuzzy-LQR control methods. Following that, the computer environment is used to simulate the designed controllers. The success of the control performance of the applied methods concerning the passive suspension system is compared in percentages. The results are presented and evaluated graphically and numerically. The methods are contrasted against one another and the research in the literature using the integral time-weighted absolute error (ITAE) criterion. As a result, it is found that the proposed control method (fuzzy-LQR) is about 84.2% more successful in body motion, 90% in car acceleration, 84.5% in suspension deflection, and 86.7% in tire deflection compared to the studies in the literature. All these results show that the car's ride comfort is significantly improved. The following is a concise list of the study's main contributions. 1. By utilizing the particle swarm optimization (PSO) algorithm, obtaining the weights of the points where the membership functions of the fuzzy-LQR control method touch, and applying this control method to the ASS, the system gains the ability to make its own decision to optimally determine the critically important Q and R parameters in the control of the system. This is the first major contribution of our study.