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This study presents a sliding mode‐based active disturbance rejection control (SMADRC) scheme for a steer‐by‐wire (SbW) system in road vehicles. First, a plant model that describes the mechanical dynamics of the SbW system is elaborated, where the viscous friction and the self‐aligning torque are regarded as external disturbances. Second, the design of SMADRC is exposited, in which a non‐linear extended state observer is utilised to estimate the non‐linearities existing in the plant model, and a sliding mode control component is used to cope with the effect of the non‐linearities and guarantee the control robustness against system uncertainties and varying road conditions. Finally, experimental results are shown to demonstrate the superiority of the designed SMADRC in comparison with a conventional sliding mode controller and a PD‐based active disturbance rejection controller (PDADRC).

This article considers the application of a robust control technique for vehicle steer-by-wire (VSbW) system subjected to variations in parameters based on adaptive integral sliding mode control (AISMC). The AISMC has been designed to control the VSbW system to cope with the uncertainties in system parameters. The proposed adaptive control scheme provides the solution for perturbation boundedness, as there is no need to have a prior knowledge of perturbation bound in the uncertainty. In addition, the proposed adaptive control design can avoid overestimation of sliding gain under unknown prior knowledge of perturbations. Moreover, the inclusion of integral sliding mode control (ISMC) leads to elimination of the reaching phase in trajectory solution of controlled system. Computer simulations have been used to verify the effectiveness of proposed AISMC to show the superiority of the proposed control technique; in this regard, a comparison between AISMC and other control methods from the literature were conducted. The numerical simulation based on MATLAB programming software showed that the designed AISMC has better tracking performance and accuracy as compared to ISMC and other control schemes in terms of robustness characteristics.

In this research paper, a recent robust control scheme was proposed and designed for a VSbW (vehicle steer-by-wire) system. Using an integral sliding mode control (ISMC) design based on barrier function (ISMCbf) could improve the robustness of ISMCs. This control scheme, due to the characteristics of the barrier function, can improve the robustness of the proposed controller better than that based on the conventional SMC or integral SMC (ISMC). The ISMCbf scheme exhibits all the benefits of the conventional ISMC with the addition of two main advantages: it does not require prior knowledge of perturbation bounds or their derivatives, and it can effectively eliminate the chattering phenomenon associated with the classical ISMC due to the smooth characteristics of the barrier function. On the other hand, in terms of the design implementation, the ISMCbf is simpler than the ISMC. In this study, the mathematical dynamical model of the VSbW plant was first presented. Then, the control design of the ISMCbf scheme was developed. The numerical results showed that the proposed scheme is superior to the conventional ISMC. The superiority of the proposed ISMCbf controller versus the classical ISM has been evaluated under three different uncertain conditions, and three scenarios can be deduced: a slalom path, quick steering, and shock disturbance rejection. Furthermore, a comparative analysis with other controllers from the literature has further been established to show the effectiveness of the proposed ISMCbf.

The modern steer-by-wire (SBW) systems represent a revolutionary departure from traditional automotive designs, replacing mechanical linkages with electronic control mechanisms. However, the integration of such cutting-edge technologies is not without its challenges, and one critical aspect that demands thorough consideration is the presence of nonlinear dynamics and communication network time delays. Therefore, to handle the tracking error caused by the challenge of time delays and to overcome the parameter uncertainties and external perturbations, a robust fast finite-time composite controller (FFTCC) is proposed for improving the performance and safety of the SBW systems in the present article. By lumping the uncertainties, parameter variations, and exterior disturbance with input and output time delays as the generalized state, a scaling finite-time extended state observer (SFTESO) is constructed with a scaling gain for quickly estimating the unmeasured velocity and the generalized disturbances within a finite time. With the aid of the SFTESO, the robust FFTCC with the scaling gain is designed not only for ensuring finite-time convergence and strong robustness against time delays and disturbances but also for improving the speed of the convergence as a main novelty. Based on the Lyapunov theorem, the closed-loop stability of the overall SBW system is proven as a global uniform finite-time. Through examination across three specific scenarios, a comprehensive evaluation is aimed to assess the efficiency of the suggested controller strategy, compared with active disturbance rejection control (ADRC) and scaling ADRC (SADRC) methods across these three distinct driving scenarios. The simulated results have confirmed the merits of the proposed control in terms of a fast-tracking rate, small tracking error, and strong system robustness.

This paper presents the development process of a robust and ROS-based Drive-By-Wire system designed for an autonomous electric vehicle from scratch over an open source chassis. A revision of the vehicle characteristics and the different modules of our navigation architecture is carried out to put in context our Drive-by-Wire system. The system is composed of a Steer-By-Wire module and a Throttle-By-Wire module that allow driving the vehicle by using some commands of lineal speed and curvature, which are sent through a local network from the control unit of the vehicle. Additionally, a Manual/Automatic switching system has been implemented, which allows the driver to activate the autonomous driving and safely taking control of the vehicle at any time. Finally, some validation tests were performed for our Drive-By-Wire system, as a part of our whole autonomous navigation architecture, showing the good working of our proposal. The results prove that the Drive-By-Wire system has the behaviour and necessary requirements to automate an electric vehicle. In addition, after 812 h of testing, it was proven that it is a robust Drive-By-Wire system, with high reliability. The developed system is the basis for the validation and implementation of new autonomous navigation techniques developed within the group in a real vehicle.

This research delves into how Model Reference Adaptive Control (MRAC) can be applied in an independent Steer-by-Wire (SBW) system, utilising a detailed 14 Degrees of Freedom (DOF) full-vehicle model. This study is all about pushing forward vehicle dynamics and control using SBW technology. This study also has come up with some cutting-edge control algorithms that allow each wheel to be steered independently, which seriously boosts how manoeuvrable and responsive the vehicle is. Through simulations, the study shows that MRAC is a big improvement over traditional control methods where quantitative analysis shows that MRAC reduces yaw rate errors by up to 66.67% compared to Proportional-Integral-Derivative (PID) and 50% compared to Multi-order PID (MOPID). Additionally, in lateral acceleration and sideslip angle controls, MRAC demonstrates a similar reduction in errors, significantly outperforming PID and MOPID with errors maintained well below 10%, proving its worth in predictive control and real-time adaptability to various road conditions and driver intentions. The key finding from this study is that MRAC greatly enhances manoeuvrability and responsiveness compared to standard methods which offer flexibility according to the different driving scenarios. There are notable advancements in vehicle steering systems, which contribute to safer and more efficient driving. In essence, this work marks a significant step forward in automotive steering technology, opening the door to safer and more efficient modern vehicles.

A crucial role is played by steering-angle prediction in the control of autonomous vehicles (AVs). It mainly includes the prediction and control of the steering angle. However, the prediction accuracy and calculation efficiency of traditional YOLOv5 are limited. For the control of the steering angle, angular velocity is difficult to measure, and the angle control effect is affected by external disturbances and unknown friction. This paper proposes a lightweight steering angle prediction network model called YOLOv5Ms, based on YOLOv5, aiming to achieve accurate prediction while enhancing computational efficiency. Additionally, an adaptive output feedback control scheme with output constraints based on neural networks is proposed to regulate the predicted steering angle using the YOLOv5Ms algorithm effectively. Firstly, given that most lane-line data sets consist of simulated images and lack diversity, a novel lane data set derived from real roads is manually created to train the proposed network model. To improve real-time accuracy in steering-angle prediction and enhance effectiveness in steering control, we update the bounding box regression loss function with the generalized intersection over union (GIoU) to Shape-IoU_Loss as a better-converging regression loss function for bounding-box improvement. The YOLOv5Ms model achieves a 30.34% reduction in weight storage space while simultaneously improving accuracy by 7.38% compared to the YOLOv5s model. Furthermore, an adaptive output feedback control scheme with output constraints based on neural networks is introduced to regulate the predicted steering angle via YOLOv5Ms effectively. Moreover, utilizing the backstepping control method and introducing the Lyapunov barrier function enables us to design an adaptive neural network output feedback controller with output constraints. Finally, a strict stability analysis based on Lyapunov stability theory ensures the boundedness of all signals within the closed-loop system. Numerical simulations and experiments have shown that the proposed method provides a 39.16% better root mean squared error (RMSE) score than traditional backstepping control, and it achieves good estimation performance for angles, angular velocity, and unknown disturbances.

As one of the advanced automotive chassis technologies, the steer-by-wire system offers a high level of precision, responsiveness, and controllability in the driving experience. It can also adjust and optimize parameters to adapt to the preferences of different drivers. However, when faced with the steer-by-wire system, both experienced drivers and novice drivers are in the novice stage, exhibiting learning or adaptation behaviors when using this steering system. In this paper, a small-scale pilot evaluation was conducted by means of a questionnaire survey and driving-simulator experiment, and the learning behavior and adaptability of four experienced and four novice drivers to the steer-by-wire system were analyzed when using the traditional steering system. The study found that experienced drivers show significant changes in their adaptation to the steering system, mainly due to their habitual driving with traditional steering systems. In contrast, novice drivers show no significant changes in their adaptation to the steering system, which is attributed to their lack of driving experience and skills, resulting in less sensitivity to changes in the steering system. Additionally, the study found that novice drivers under the steer-by-wire system grasp control over speed and steering-wheel angle more quickly. This research provides a reference for improving drivers’ learning and adaptation abilities to the steer-by-wire system and optimizing the design of the steer-by-wire system.

Steering feel is a crucial factor in Steer-by-Wire (SbW) architecture, where achieving the desired physical feedback is essential for driver experience. This research proposes the application of an adaptive Proportional-Integral (PI)-based controller as a torque tracking algorithm to achieve the targeted steering feel. To support this, a 14-DOF vehicle dynamic model was developed and validated in order to provide the foundation for a torque tracking algorithm to generate steering feel at the steering wheel. In the process of steering feel development, the SbW model was developed using a physical modeling approach based on Newton’s laws, where the steering torque felt by the driver at the wheel corresponds to the torque generated by the rack and pinion assembly. The adaptive tracking torque algorithm is implemented using two methods, which are adaptive PI-based (A-PI) and gain scheduling PI-based (GS-PI) methods. The performance of these controllers was verified by using types of input signals (step and sinusoidal wave) and validated through three vehicle dynamic tests based on the ISO standards, including Double Lane Change, Slalom, and J-Turn tests. The findings showed that the GS-PI controller exhibited significant performance compared to results from the CarSim software, which served as a benchmark. Furthermore, the results indicated that this controller was capable of effectively replicating the desired steering feel with approximately less than 2.5 mean absolute error (MAE). This research study offers a practical control strategy for realistic steering feedback and contributes valuable insight for future SbW development.

The automotive industry, with particular reference to the off-road sector, is facing several challenges, including the integration of Advanced Driver Assistance Systems (ADASs), the introduction of autonomous driving capabilities, and system-specific requirements that are different from the traditional car market. Current vehicular electrical–electronic (E/E) architectures are unable to support the amount of data for new vehicle functionalities, requiring the transition to zonal architectures, new communication standards, and the adoption of Drive-by-Wire technologies. In this work, we propose an automated methodology for next-generation off-road vehicle E/E architectural design. Starting from the regulatory requirements, we use a MILP-based optimizer to find candidate solutions, a discrete event simulator to validate their feasibility, and an ascent-based gradient method to reformulate the constraints for the optimizer in order to converge to the final architectural solution. We evaluate the results in terms of latency, jitter, and network load, as well as provide a Pareto analysis that includes power consumption, cost, and system weight.