## 5 Conclusions  
In this paper, the novel AADRC scheme is designed for the SBW with disturbances and time delays induced by the transmission channel to improve the transient rate and steady-state tracking accuracy of the steer angle performance. To diminish the impacts of large time delays in communication, Taylor series expansion is employed for the time-delayed second-order SBW system. Then, the impact of not only uncertainties, disturbances, and time delays is diminished, but also the increased order by the Taylor transformation is well dealt with by the proposed AADRC approach. The novel AADRC scheme comprises the AESO and AFSEFBC elements. First, the AESO is built to observe unmeasured velocity, acceleration states, and the lumped disturbance, in which adaptive gains of the AESO are varied online according to the estimation error of the steering angle to minimize the peaking of the estimation. Second, the AFSEFBC is constructed to attain steering tracking control, in which its gains are driven depending on the tracking error of the steering angle. Finally, the convergence and boundedness analysis of the closed-loop dynamics of both the AESO and AFSEFBC are mathematically presented in the presence of large time delays and unknown disturbances. Simulation outcomes verify the superiority of the proposed control algorithm in comparison to the well-known ADRC technique with fixed gains.  
Future research should focus on the potential applications of the proposed control strategy to other x-by-wire technologies (i.e., x-by-wire including drive/brake-by-wire systems) with time delays induced from the NCSs and other automobile applications, such as automobile electronic throttle systems subject to more sophisticated probability density function distribution disturbances describing the influence of uncertainty propagation on those systems. More specifically, despite the challenges associated with obtaining an accurate mathematical representation of lumped uncertainty in practice, we can still leverage statistical methods to model this uncertainty as a random input to the system. Utilizing probability density functions, we can approximate and analyze the corresponding system output triggered by this uncertainty [37].  
**Author Contributions:** Conceptualization, K.R., Z.C. and Z.M.; methodology, K.R. and Z.C.; software, K.R.; validation, K.R., Y.Z., Z.C. and Z.M.; formal analysis, K.R. and Y.Z.; investigation, K.R., Z.C. and Y.Z.; resources, Z.C. and Z.M.; writing--original draft preparation, Y.Z. and K.R.; writing--review and editing, K.R., Y.Z., Z.C. and Z.M.; visualization, Y.Z., K.R., Z.C. and Z.M.; supervision, Z.C. and Z.M.; project administration, Z.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the Australian Research Council Discovery Project under Grant DP190101557.  
Not applicable.  
**Data Availability Statement:** Data sharing is not applicable to this article.  
**Acknowledgments:** [PERSON] would like to appreciate the Republic of Iraq/Ministry of Higher Education and Scientific Research and the University of Baghdad/Al Khwarizmi College of Engineering for granting a Research Fellowship.  
**Conflicts of Interest:** The authors declare no conflicts of interest.