## 1 Introduction
[31] proposed an adaptive neural network (NN) as a control method for a four-car model with time-varying vertical displacement and speed limit and an ASS with unknown car mass. The unknown car mass was inserted into the simulation environment using the NN used in the method. [PERSON] et al. [32] proposed and experimentally applied a new method for active suspension systems using model-independent finite-time motion control methods. In this method, the vertical dynamics of the suspension system, including the external disturbances, were estimated by the time delay estimation. The estimated error was compensated using the integral sliding mode control method. The experimental results showed that the method performed adequately as long as the vertical displacement of the sprung mass and the vertical car acceleration reduced the RMS error compared with the traditional suspension system methods. [PERSON] et al. [33] proposed the fuzzy sliding mode control method with a proportional differential sliding mode observer for an ASS. It was shown that the fuzzy sliding mode control method in the simulation environment could improve the ride comfort, operational stability, and driving safety of the car, and reduce energy consumption. [PERSON] et al. [34] proposed a fuzzy sliding mode-based anti-disruptive control for a 7-DOF active car suspension system. An extended state observer was used to estimate all disturbances in the system. The fuzzy sliding mode control for active distortion suppression (FSM-ADRC) was compared with existing active distortion suppression methods (ADRC and SM-ADRC). This comparison showed that the proposed method performed better. [PERSON] et al. [35] proposed an adaptive optimal control with neural networks and output feedback for ASSs and implemented it in a simulation environment. In this study, NNs were used to identify unknown nonlinear states, and an NN state observer was used to predict unmeasurable states. The proposed optimal control algorithm ensured the boundedness of the signals of the controlled system, and the power of the control input and the amplitude of the vertical displacement was minimized. [PERSON] and [PERSON] proposed an adaptive harmonic control for the active suspension of a quarter of a car and applied it in a simulation environment [36]. The method was compared with the classical PI controller, the Ho controller, and the model predictive control method. The comparison results showed that the harmonic controller performed better than control algorithms for some performance criteria. Using the least squares method, [PERSON] et al. [37] optimized and implemented a passive suspension system as an active FLC system. This study compared passive systems, traditional FLC, optimized passive systems, and optimized FLC methods. The optimized passive suspension system obtained 30% and 27% lower RMS acceleration and VDV values than the original. The governor force was reduced by 38% compared to the original FLC system. VDV and RMS acceleration and maximum acceleration were also reduced by 32%, 28%, and 24%, respectively. [PERSON] et al. (2018) proposed an LQR control method using the BAT algorithm for car suspension system control, which was applied in an experimental environment on different road profiles. The study also compared the effectiveness of the method with the GWO algorithm. Regarding the road profile with bumps, the percentage reduction in the RMS value of trunk acceleration for the proposed BA-adjusted LQR was 64.6%, while the GWO-adjusted LQR was 37.5%. Similarly, for the impulse path profile, the BA-adjusted LQR resulted in a 43% reduction and the GWO-adjusted LQR resulted in a 25.2% reduction in the RMS value of hull acceleration compared to the conventional LQR. The proposed control method increased the ride comfort despite the rough road profile. [PERSON] et al. (2019) proposed an LQR control method using the active suspension system's ant colony optimization (ACO). They applied it to the model of a quarter car in an experimental environment.