Search Results (5)

The steer-by-wire (SBW) system represents an optimal solution for achieving intelligent vehicle steering. However, the current reliability of SBW motors and electronic control units remains limited. Disturbances, including variations in the external road environment and time-varying parameters, can significantly impact vehicle stability. To address these challenges, a hierarchical control strategy is proposed in this paper. In the upper layer, model predictive control (MPC) is employed to optimize the sideslip angle and yaw rate by tracking their reference values, thereby enhancing the stability of the SBW system. In the lower layer, a composite reaching law sliding mode control based on an extended state observer (ESO-CRLSMC) is developed to address dual-motor parameter mismatch and speed synchronization issues, thereby ensuring the reliability of the dual-motor system. Finally, hardware-in-the-loop experiments demonstrate that under time-varying disturbances and parameter mismatches, the proposed controller not only ensures vehicle handling stability but also improves steering response speed, robustness, and synchronization performance. Full article

Distributed drive vehicles provide enhanced actuation flexibility, making longitudinal–lateral coordinated stability control essential for improving vehicle handling and safety under complex driving conditions. Nevertheless, the existing coordinated control strategies commonly employ stability reference models with fixed tire–road friction coefficients, which restrict their adaptability to time-varying adhesion environments. In addition, conventional sliding mode-based lateral stability controllers may exhibit limited performance when confronted with strong nonlinear coupling and external disturbances. To address these issues, this paper proposes an integrated longitudinal–lateral coordinated stability control framework for distributed drive vehicles. A dual unscented Kalman filter-based estimator is developed to identify the tire–road friction coefficients and construct a friction-adaptive reference model for yaw rate and sideslip angle. An adaptive fractional power speed controller with resistance compensation is designed to generate the total longitudinal driving torque, while an adaptive neural sliding mode controller produces the corrective yaw moment for lateral stability enhancement. Furthermore, a pseudoinverse-based torque distribution strategy is employed to allocate the longitudinal torque and yaw moment to individual wheels. Simulation results demonstrate that the proposed framework significantly improves vehicle stability and tracking accuracy compared with conventional control methods under varying road conditions. 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

A fault-tolerant collaborative control strategy for four-wheel-drive electric vehicles is proposed to address hidden safety issues caused by one or more in-wheel motor faults; the basic design scheme is that the control system is divided into two layers of motion tracking and torque distribution, and three systems, including driving, braking, and front-wheel steering are controlled collaboratively for four-wheel torque distribution. In the layer of motion tracking, a vehicle model with two-degree-of-freedom is employed to predict the control reference values of the longitudinal force and additional yaw moment required; four types of sensors, such as wheel speed, acceleration, gyroscope, and steering wheel angle, are used to calculate the actual values. At the torque distribution layer, SSOD and MSCD distribution schemes are designed to cope with two operating conditions, namely sufficient and insufficient output capacity after local hub motor failure, respectively, focusing on the objective function, constraints, and control variables of the MSCD control strategy. Finally, two operating environments, a straight-line track, and a DLC track, are set up to verify the effectiveness of the proposed control method. The results indicate that, compared with traditional methods, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 12.9% and 5.88%, respectively, in the straight-line track environment. In the DLC track environment, the average errors of the center of mass sideslip angle and yaw rate are reduced by at least 6% and 4.5%, respectively. The proposed fault-tolerant controller ensures that the four-wheel-drive electric vehicle meets the requirements of handling stability and safety under one or more hub motor failure conditions. Full article

Distributed drive electric vehicles (DDEV), characterized by their independently drivable wheels, offer significant advantages in terms of vehicle handling, stability, and energy efficiency. These attributes collectively contribute to enhancing driving safety and extending the all-electric range for sustainable transportation. Nonetheless, the challenge persists in designing a control strategy that effectively coordinates the objectives of handling, stability, and energy efficiency under both lateral and longitudinal driving conditions. To this end, this paper proposes a cooperative control strategy for DDEVs, incorporating active rear steering (ARS) and direct yaw moment control (DYC) to enhance handling capabilities, stability, and energy efficiency. A stability boundary is delineated using an analytical expression that correlates with the front wheel steering angle, and an adjustment factor is introduced to quantify vehicle stability based on this input parameter. This factor aids in establishing a coordinated control reference for handling and stability. At the upper-level motion control layer, a model predictive control method is developed to track this reference and implement ARS and DYC for superior performance. Specifically, the rear lateral force serves as the control command for ARS, which is converted into a rear wheel steering angle using a tire inverse model. Meanwhile, the front lateral force is modeled as linear-time-varying to simplify calculations. At the lower-level torque allocation layer, the adjustment factor is utilized to balance tire workload rate and in-wheel motors’ (IWM) energy consumption, enabling efficient switching between energy consumption and driving stability targets, and the torque allocation is conducted to acquire the expected IWMs’ command. Both the upper and lower-level optimization problems are formulated as convex problems, ensuring efficient and effective solutions. Simulations verify the effectiveness of this strategy in improving handling, stability, and energy economy under DLC cases, while maintaining high computational efficiency. Full article