Search Results (64)

This manuscript investigates the feasibility of Four-In-Wheel Electronic Differential Systems (4 IW-EDSs) within contemporary electric vehicles (EVs), emphasizing their benefits for stability regulation predicated on steering angles. Through an extensive literature review, we conduct a comparative analysis of various in-wheel-motor models in terms of power output, efficiency, and torque characteristics. Furthermore, we explore the distinctions between IW-EDSs and steer-by-wire systems, as well as conventional systems, while evaluating recent research findings to determine their implications for the evolution of electric mobility. Moreover, this paper addresses the necessity for fault-tolerant methodologies to boost reliability in practical applications. The findings yield valuable insights into the challenges and impacts associated with the implementation of differential steering control in four-wheel independent-drive electric vehicles. This study aims to explore the interaction between these systems, optimize torque distribution, and discover the most ideal control strategy that will improve maneuverability, stability, and energy efficiency, thereby opening up new frontiers in the development of next-generation electric vehicles with unparalleled performance and safety features. Full article

This paper presents a high-order model-based robust control strategy for a dual-motor road wheel actuating system in a steer-by-wire (SbW) architecture. The system consists of a belt-driven and a pinion-driven motor collaboratively actuating the road wheels through mechanically coupled dynamics. To accurately capture the interaction between actuators, structural compliance, and road disturbances, a four-degree-of-freedom (4DOF) lumped-parameter model is developed. Leveraging this high-order dynamic model, a composite control framework is proposed, integrating feedforward model inversion, pole-zero feedback compensation, and a disturbance observer (DOB) to ensure precise trajectory tracking and disturbance rejection. High-fidelity co-simulations in MATLAB/Simulink and Siemens Amesim validate the effectiveness of the proposed control under various steering scenarios, including step and sine-sweep inputs. Compared to conventional low-order control methods, the proposed approach significantly reduces tracking error and demonstrates enhanced robustness and disturbance attenuation. These results highlight the critical role of high-order modeling in the precision control of dual-motor SbW systems and suggest its applicability in real-time, safety-critical vehicle steering applications. Full article

Aiming at the problem of the insufficient stability of the unified model of steering angle transmission ratio at high speeds, we introduce a novel control strategy that integrates the yaw rate gain, lateral acceleration gain, vehicle speed and steering wheel angle, achieving great improvements in a simulation. The new control strategy uses a genetic algorithm to optimize the yaw rate and lateral acceleration gain values at different speeds, and the two are weighted. The ideal variable-angle transmission ratio control strategy is designed by using the unified model of steering angle transmission ratio at different speed intervals. The simulation results show that the strategy reduces the steering wheel angle peak by 67.12% compared with the fixed-angle transmission at low speeds. Compared with the unified model of steering angle transmission ratio at high speeds, the peak values of the yaw rate, the lateral acceleration and sideslip angle of the vehicle are reduced by 7%, 5.67% and 11.67%, respectively, which effectively improves the steering stability of the vehicle. Full article

Aiming at the characteristic of a variable and optimized steering ratio of the Steer-by-Wire System (SBW), this paper studies the design method of the steering ratio starting from the influence of the steering ratio on the vehicle steering maneuverability and the driver’s steering burden. Through the analysis of the influencing factors of the steering ratio and the analysis of the driver’s steering characteristics, a yaw rate gain control model is established. Combined with the evaluation index of handling stability, the yaw rate gain is optimized, and the optimal yaw rate gain corresponding to different scenarios and different drivers’ steering characteristics is determined, so as to design the characteristics of the variable steering ratio that meet the preferences of different drivers. In order to verify the control effect of the variable gain steering ratio, a comprehensive feedback control strategy for the front wheel angle is established, and vehicles with a fixed steering ratio and a constant gain steering ratio are selected as references. Comparative tests under typical working conditions are carried out in the “driver-vehicle-road” closed-loop simulation system. The results show that the variable gain steering ratio considering the driver’s steering characteristics can not only improve the handling stability of the vehicle at medium and high speeds, but also enhance the driver’s steering comfort, enabling the SBW to achieve the goal of “the vehicle adapting to the person”. Full article

Addressing the shortcoming that steer-by-wire (SBW) system cannot directly transmit road feel, this study investigates a SBW system dynamics model, steering angle tracking control, and road feel simulation algorithm design. This study proposes a high-precision observer-based road feel simulation method that achieves road feel feedback torque design through the real-time estimation of system disturbance torque based on accurate front-wheel angle tracking. The methodology employs an improved nonlinear second-order sliding mode observer (INSOSMO) to estimate the system disturbance torque. This observer incorporates proportional–integral terms into the super-twisting algorithm to enhance dynamic response, replaces the sign function with a Sigmoid function to eliminate chattering, and utilizes the sparrow search algorithm (SSA) for global parameter optimization. Meanwhile, a two-stage filter combining a strong tracking Kalman filter (STKF) and first-order low-pass filtering processes the observer values to generate road feel feedback torque. Additionally, for the active return control of the steering wheel, a backstepping sliding mode control (BSSMC) integrated with an extended state observer (ESO) is employed, where the ESO enhances the robustness of BSSMC through real-time nonlinear disturbance estimation and compensation. MATLAB/Simulink-CarSim co-simulation demonstrates that, under sinusoidal testing, the INSOSMO reduces mean absolute error (MAE) by 34.7%, 62.5%, and 60.1% compared to the ESO, Kalman filter observer (KFO), and conventional sliding mode observer (SMO), respectively. The designed road feel feedback torque meets operational requirements. The active return controller maintains accurate steering wheel repositioning across various speed ranges. Full article

To improve the maneuverability and agility of jumping robots, a variety of steerable jumping mechanisms have been actively studied. The steering ability enables a robot to reach a particular target by altering its jumping direction. To make this possible, we propose a miniature steerable jumping robot based on froghopper’s jumping principle: Moment cancellation is achieved via synchronous leg rotation, and a predictable jumping direction is achieved through an almost zero stiffness femoro-tibial joint. To satisfy these working principles, the robot is designed to have a four-bar shaped body structure and wire-driven knee joints. The four-bar body always synchronizes the leg operation by mechanically coupling the two jumping legs, which enables the robot to cancel out the moments and finally reduce the needless body spin. The knee joints are actuated using wires, and the wires are kept loose to maintain joint stiffness almost zero during take-off. Accordingly, the jumping direction is successfully predicted to determine the initial posture of the tibia. As a result, the proposed robot can change the jumping direction from −20 degrees to 20 degrees while reducing needless body spin. Full article

Real-time estimation of the road surface friction coefficient is crucial for vehicle dynamics control. Under large steering angles, the accuracy of existing road surface friction coefficient estimation methods is unsatisfactory due to the nonlinear characteristics of the tire. This paper proposes a segmented estimation method for the road adhesion coefficient, which considers different steering angle ranges and utilizes multimodal vehicle dynamics fusion. The method is designed to accurately estimate the road adhesion coefficient across the full steering angle range of the steer-by-wire system. When the front wheel angle is small (less than 2.8°), an improved Unscented Kalman Filter (AUKF) algorithm is used to estimate the road surface friction coefficient. When the front wheel angle is large (greater than 3.2°), a rack force expansion state observer is constructed using the dynamics model of the steer-by-wire actuator to estimate the rack force. Based on the principle that the rack force varies with different road surface friction coefficients for the same steering angle, the rack force is used to distinguish the road surface friction coefficient. When the front wheel angle is between the two ranges, the average value of both methods is taken as the final estimate. The method is verified through Matlab/Simulink and CarSim co-simulation, as well as hardware-in-the-loop experiments of the steer-by-wire system. Simulation results show that the relative error of road surface friction coefficient estimation is less than 10% under different steering angles. The segmented combination estimation strategy proposed in this paper reduces the impact of tire nonlinearities on the estimation result and achieves high-precision road surface friction coefficient estimation over the entire steering angle range of the steer-by-wire system, which is of significant importance for vehicle dynamics control. Full article

The drive torque of each wheel hub motor of a four-wheel independent wire-controlled steering electric vehicle is independently controllable, representing a typical over-actuated system. Through optimizing the distribution of the drive torque of each wheel, fault-tolerant control can be realized. In this paper, the four-wheel independent wire-controlled steering electric vehicle is taken as the research object, aiming at the collaborative control problem of trajectory tracking and yaw stability when the actuator of the by-wire steering system fails, a fault-tolerant control method based on the synergy of differential steering and direct yaw moment is proposed. This approach adopts a hierarchical control system. The front wheel controller predicts the necessary steering angle in accordance with a linear model and addresses the requirements of the front wheels and additional torque. Subsequently, considering the uncertainties in the drive control system and the complexities of the road obstacle model, the differential steering torque is computed via the sliding mode control method; the lower-level controller implements the torque optimization distribution strategy based on the quadratic programming algorithm. Finally, the validity of this approach under multiple working conditions was verified via CarSim 2019 and MATLAB R2023b/Simulink simulation experiments. Full article

This paper proposes a novel steer-by-wire (SBW) system for marine vessels as a viable alternative to conventional hydraulic steering systems. By replacing mechanical linkages, the proposed SBW system enhances responsiveness, reduces complexity, and minimizes operator fatigue. Designed with a power transmission mechanism suited to maritime environments, it features a modular architecture that allows for seamless integration into existing vessels. Onboard experimental studies quantify the forces required for steering, establishing design criteria for the SBW system, while a disturbance observer (DOB)-based velocity controller improves tracking performance under unpredictable maritime conditions. Moreover, a sensorless admittance control strategy enables steering-feel rendering without the need for additional force sensors, thereby simplifying the overall design. Analyses of stiffness and damping characteristics further reveal that individual and combined tuning of these coefficients allows for customizable steering feel tailored to diverse operator requirements. Full article

To enhance the driving experience for drivers, making it more realistic and comfortable, this study proposes a simulation control strategy for steering-by-wire road sense based on sliding mode control. Firstly, a dynamic model is constructed for the steering wheel module and the steering actuator, building upon this, a methodology for calculating the self-aligning torque is formulated, utilizing the linear two-degree-of-freedom vehicle model in conjunction with the Magic Formula. The approach incorporates the influences of assistive forces, damping, and frictional torque, thereby providing an accurate simulation of the steering road feel. On this basis, a sliding mode control algorithm is developed to achieve the necessary road-sensing motor current, guaranteeing system stability and rapid response across various driving scenarios. To validate the simulation, MATLAB/Simulink in conjunction with Carsim is employed. The outcomes from tests involving dual lane shifts and step inputs demonstrate that the introduced control approach effectively follows the target current, featuring swift convergence and minimal response latency. Full article

Steer-by-wire technology, critical for autonomous driving, enables full-wheel steering in articulated vehicles, significantly enhancing maneuverability in complex driving environments. This study investigates dynamic envelope optimization for articulated multi-body vehicles by integrating coordinated multi-axle steering control strategies with higher-order Bezier curve designs. Unlike traditional approaches that primarily focus on single-axle steering, this research emphasizes the advantages of multi-axle steering control, which significantly reduces the dynamic envelope and enhances maneuverability. To address the challenges posed by constrained road environments, a comparative analysis of Septimic Bezier curves under various control point configurations was conducted, demonstrating their effectiveness in achieving smoother curvature transitions and steering comfort. The results highlight the pivotal role of reducing curvature peaks and increasing curvature continuity in optimizing vehicle performance. Furthermore, advanced steering control strategies, such as Articulation Angle Reference (AAR) and Dual Ackermann Steering (DAS), were shown to outperform conventional methods by ensuring precise trajectory control and improved stability. This study provides actionable insights for enhancing vehicle handling and safety in complex driving scenarios, offering a framework for future road design and multi-axle steering system development. Full article

Steer-by-wire (SbW) is the latest steering evolution, providing many benefits, such as reduced vehicle weight and enhanced steering capability. While reliability is one of the main concerns hindering its widespread application, the potential of this technology to revolutionize the automotive industry is huge. Control techniques play an important part in achieving the full potential of SbW by focusing on the development of its performance and safety aspects. This paper provides a review of the control techniques that are being used in SbW technology to achieve better performance and enhance safety. Various control techniques for SbW exist to serve different purposes when dealing with the non-linear nature of the SbW dynamics. Although there is no one-size-fits-all control technique for all the requirements of the SbW, this study highlights that the non-linear controllers based on the sliding mode control (SMC) are a popular option for enhancing the accuracy of SbW systems. However, these controllers often suffer from a downside known as chattering. In contrast, robust controllers like Model Predictive Controllers (MPCs) can effectively manage uncertain dynamics and eliminate chattering, although they pose challenges due to their high computational costs. Full article

To enhance the control performance of a wire-controlled steering system, an improved sparrow search algorithm for fine-tuning the gains of a proportional–integral–derivative (SSA-PID) steering motor control algorithm is proposed. Mathematical models of the steering system and motor were derived based on an analysis of the system’s structure and dynamics. A PID controller was developed with the aim of facilitating the precise control of the steering angle by targeting the angle of the steering motor. The population diversity in the sparrow algorithm was enhanced through the integration of a human learning mechanism along with a Cauchy–Gaussian variation strategy. Furthermore, an adaptive warning strategy was implemented, which employed spiral exploration to modify the ratio of early warning indicators, thereby augmenting the algorithm’s capacity to evade local optima. Following these enhancements, an SSA-PID steering motor control algorithm was developed. Joint simulations were performed using the CarSim software 2019.1 and MATLAB/Simulink R2022a, and subsequent tests were conducted on a wire-controlled steering test rig. The outcomes of the simulations and bench tests demonstrate that the proposed SSA-PID regulation algorithm is capable of adapting effectively to variations and disturbances within the system, facilitating precise motor angle control and enhancing the overall reliability of the steering system. Full article

The integrity of the $12\mathrm{V}\mathrm{dc}$ power distribution system on a vehicle is essential to guarantee continuous power supply to safety-relevant consumers. Safety-relevant consumers are critical loads, for example, electric power steering, braking systems with functionalities like Anti-Lock Braking or Electronic Stability Control, and autonomous drive systems. To prevent insufficient power supply for safety-relevant consumers due to an increased wiring harness resistance, a novel diagnostic approach is developed to determine the condition of the power distribution, especially the electrical resistance. The influence of measurement errors and bus commutation on the estimation is investigated by using a simulation. By using the diagnostic, a resistance determination in the milliohm range with a standard deviation of $\sigma =0.3\mathrm{m}\Omega$ can be achieved under realistic conditions. This ensures that failures in the wiring harness can be identified, avoiding cascading effects and minimizing recalls. Compared to the state of the art, redundancies, costs, and weight can be saved with the proposed diagnostic system based on electrical resistance estimation. Full article

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. Full article