## 1 Introduction
[23] proposed an adaptive neural network control method for the ASS with actuator saturation and applied it in a simulation environment. A YSA observer was used for state prediction using the system's measured input and output data. The study optimized the feedback control parameters using PSO based on the state observer. The method was compared with the passive state suspension system, neural sliding mode control (SMCNN), and traditional neural network (TNN) control methods. In the simulation results, body vibration was effectively suppressed, the values of the proposed controller RMS were improved under different road conditions, and the body displacement results were obtained at a lower value than the SMCNN controller and TNN controller. [PERSON] et al. [24] proposed an adaptive control method for ASSs to improve the vertical dynamic performance of the car in the presence of parameter uncertainties, disturbing inputs, and non-ideal actuators. Comparative simulations were performed. Compared with existing control methods, the presented control method was adaptive to parameter uncertainties, and it was shown to reduce the effects of non-ideal actuators in the simulation environment. [PERSON] et al. [25] proposed a fuzzy control method for uncertain active car suspension systems with a dynamic sliding mode approach. They applied it to a quarter-car model in a simulation environment. This study used a T-S fuzzy method to consider the change in masses. The sliding mode control parameters were linearly generated and a dynamic fuzzy term was used to construct the sliding mode controller. It was found that the method significantly reduced the spring-mass acceleration compared to passive systems and a lower body acceleration was achieved during road disturbances. [PERSON] et al. [26] proposed an Adaptive Neuro-Fuzzy Inference System (ANFIS) controller for a hydraulically actuated active suspension and applied it to a semi-auto model in a simulation environment. The proposed method was compared with passive and PID control methods for a sinusoidal trajectory profile. As a result of the simulation, it was found that the body displacement and tilt angle of the ANFIS-controlled ASS were significantly lower than those of the PID-controlled suspension system. [PERSON] et al. [27] proposed an optimal sliding mode control (OSMC) for an ASS based on a genetic algorithm and implemented it in a simulation environment. From the simulation results, the comprehensive performance index (RMS) of the OSMC controller was reduced by 12.73% and 33.4% compared to the SMC controller and passive system, respectively. [PERSON] et al. [28] implemented GA-based multicriteria optimized fuzzy logic and PID control of the active quarter-car nonlinear suspension system in a simulation environment. It was shown that the active FLC control system based on GA minimized the frequency-weighted RMS acceleration and VDV compared with PID and passive suspension systems, thus improving ride comfort. [PERSON] et al. [29] proposed an adaptive event-driven fuzzy control method for active vehicle suspension systems with uncertainties. They applied it to the model of a quarter car in the simulation environment. The T-S fuzzy model was used for suspension system uncertainties. The purpose of this method was to save communication resources. As a result of the simulation, a reliable fuzzy controller design approach based on the adaptive event-driven scheme was provided, which can effectively reduce the transmission load at all frequencies. [PERSON] and [PERSON] proposed a networked control scheme for the ASS of a complete car model [30]. The study investigated two networked control systems. PID and LQR control methods were used. They proposed a networked control system for the ASS using the CAN network model. In a networked system with low network speed, the results show that the performance of the LQR method was better than that of PID. [PERSON] et al.