###### Abstract
In addition, it can be seamlessly integrated with various control algorithms in the SBW systems [12; 13; 14; 15; 16; 17; 18; 19]. In [12], a prior equation is adopted to estimate and compensate for the self-aligning torque. Moreover, an adaptive law is tailored to determine the gain of this equation. The outcome of the prior equation is delivered to a feedback motor, enabling it to produce a responsive force that replicates the 'road feels' for the driver. In addition, it serves as a crucial uncertainty compensation in the fast non-singular terminal SMC (FNTSMC). This uncertainty compensation component effectively mitigates the impact of a self-aligning torque. However, as the prior equation may contain noise and uncertainty and it cannot be used to estimate unavailable system states such as angular velocity of the steering motor, an adaptive sliding mode observer (ASMO) as well as a Kalman filter (KF) are employed in [15] to improve estimation performance of SBW system states. To further improve the estimation rate, an adaptive fixed-time state observer (AFTSO) is proposed to compute the front wheels' speed in [17]. An adaptive SMC (ATSMC) terminal is proposed in [19]. In this work, the bounds of the system parametric uncertainty and the external disturbances are compensated by the adaptive laws in the reaching law of ATSMC. Given that disturbances in SBW systems can be categorized as a predictable part and a bounded unknown component, ref. [14] utilizes the Luenberger observer to estimate the former. In [13], the adaptive law is applied to determine the output layer parameters of the extreme learning machine (ELM) which is designed to compensate for the lumped uncertainty. As the precise bounds on some state-dependent uncertainties of the SBW systems may not be known, ref. [18] suggests the implementation of an adaptive control approach that eliminates the need for prior knowledge of uncertainty structures and bounds. Unfortunately, the aforementioned references fail to consider the negative impact of communication time delays in SBW systems, which may hinder the practical implementation of these methods. In terms of SBW systems, the control input is voltage which is utilized to regulate the steering motor, while the angular position of the front wheels serves as the feedback [12]. However, the introduction of the CAN bus in these systems introduces communication time delays in both the control input (denoted as input delay) and the feedback signal (denotedas output delay) [20; 21]. Since the input and output delays can considerably affect control performance [22; 23], it is imperative to consider them during the controller design process. Thus, ref. [21] introduces a layered time-delay robust control strategy (LTDRCS) to address large random time delays in SBW systems. Nevertheless, this strategy does not account for uncertainty estimation and compensation, and the selection of control parameters can be complex. In another recent study [24], the time-delayed knowledge of control input and system states is utilized to compute uncertainties. However, this approach necessitates precise knowledge of the past values of control input and system states, thereby posing practical implementation challenges. The human-machine interface (HMI) has found widespread applications in many automated systems, including those pertaining to SBW systems [25]. However, in highly automated systems, the design of the HMI is primarily influenced by technological advancements, rather than the expertise of the individuals who drive the vehicles. In practice, drivers may not fully comprehend the operations performed through the HMI [26]. In scenarios such as the emergence of traffic conditions, the safety of drivers is compromised. As a potential solution, it is imperative to develop HMIs for the SBW systems that offer intuitive and user-friendly information specifically tailored for drivers to enhance driving experience, usability, and safety. Another practical challenge for the SBW system lies in its fault-tolerant capability.