## 1 Introduction  
Vehicle suspension systems, which have a decisive influence on a vehicle's ride comfort and handling characteristics, have been researched from the past to the present with the development of technology [1; 2; 3; 4]. Suspension systems have the important task of supporting the vehicle's weight, keeping the tires in contact with the ground, and isolating the disturbing influences on the chassis. [PERSON] and [PERSON] proposed a fuzzy logic control method for the ASS using internal and external control loops [5]. A fuzzy logic controller is applied to the model of a quarter, where the inner loop controls the nonlinear hydraulic actuator to monitor the desired actuation force. In contrast, the outer loop is calculated by GA-based optimization. [PERSON] proposed a fuzzy logic control method for controlling the suspension system of a small car and applied it in the simulation environment [6]. Results were obtained for three different road types. The body handling and the responses in the suspension working area exhibited good damping characteristicsfor different road conditions. [PERSON] et al. [7] proposed a fuzzy logic control method with a neural network for ASSs. The method was applied to the model of a quarter car in the simulation environment and contrasted with the methods of a passive system, SMC, SMFLC, and SMNNNFLC. The simulation results show that the proposed SMNNNFLC method provides higher ride comfort and quality than the other methods mentioned in the article. [PERSON] et al. [8] proposed a proportional-integral sliding mode control method for the ASS and applied it to a quarter-car model in a simulation environment. This study compared the controller's performance with the linear quadratic controller (LQR) and the existing passive suspension system. It was shown that the proposed method is more robust compared to the linear quadratic controller method and the passive suspension system. The constrained Ho control of active suspensions, an LMI approach, was proposed by [PERSON] and [PERSON] [9]. In a simulation environment for the LMI-optimized Ho control of a 2-DOF-constrained suspension system, good handling, constraining the suspension stroke, and avoiding actuator saturation were among the issues investigated. The result of this study was that close contact of the wheels with the road was ensured, and the best possible ride comfort was achieved when the suspension shocks and control inputs were kept within the specified limits. [PERSON] et al. [10] applied the constrained Ho control to the half-car model in a simulation environment using multicriteria LMI optimization. Positive results were obtained in improving ride comfort. [PERSON] et al. [11] proposed a robust fuzzy sliding mode controller for the half-car model with an ASS. The magnitudes of trunk displacement and pitching motion were reduced and the resonance peak caused by the car body was eliminated. It was also found that the magnitudes of trunk acceleration and pitching motion were reduced over a wide range of frequencies, significantly improving ride comfort. [PERSON] et al. [12] investigated the problem of robust sampling data control for indeterminate active car suspension systems on a quarter-car model. Using an input-lag approach, the active car suspension system was transformed into a continuous-time system with state delay using sampling measurements, and polytopic parameter uncertainty was used to characterize the actual uncertain situation. [PERSON] and [PERSON] proposed a fuzzy logic control method for the ASS of a quarter car and applied it in a simulation environment [13]. This method was compared with the PID control method in two different types of railroads. The ride comfort was improved by reducing the body acceleration caused by the car body during road disturbances caused by smooth roads and real road bumps. [PERSON] and [PERSON] studied the intelligent control of ASSs in a simulation environment [14].