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Aiming at the problems of control stability of the intelligent vehicle lateral control method, single test conditions, etc., a lateral control method with feedforward + predictive LQR is proposed, which can better adapt to the problem of intelligent vehicle lateral tracking control under complex working conditions. Firstly, the vehicle dynamics tracking error model is built by using the two degree of freedom vehicle dynamics model, then the feedforward controller, predictive controller and LQR controller are designed separately based on the path tracking error model, and the lateral control system is built. Secondly, based on the YOLO-v3 algorithm, the environment perception system under the urban roads is established, and the road information is collected, the path equation is fitted and sent to the control system. Finally, the joint simulation is carried out based on CarSim software and a Matlab/Simulink control model, and tested combined with hardware in the loop test platform. The results of simulation and hardware-in-loop test show that the transverse controller with feedforward + predictive LQR can effectively improve the accuracy of distance error control and course error control compared with the transverse controller with feedforward + LQR control, LQR controller and MPC controller on the premise that the vehicle can track the path in real time.

Single-wheel brake failure in electromechanical brake (EMB) systems breaks the left-right symmetry of wheel forces and yaw moments, creating a critical conflict between emergency braking effectiveness and lateral stability. To address this symmetry-breaking condition, this paper proposes a bimodal, adaptive, coordinated fault-tolerant control strategy that integrates dynamic brake torque redistribution with active front steering (AFS). A novel dynamic interaction model linking deceleration demand with tire adhesion utilization enables real-time assessment and optimization of the balance between longitudinal braking performance and yaw stability. Braking forces are allocated based on adhesion utilization through a layered two-mode strategy—balanced distribution prioritizing lateral stability and compensatory distribution engaging the healthy front wheel when rear axle capacity is exceeded. An integral sliding-mode controller computes the additional yaw moment needed to suppress yaw-rate deviation, with rigorous Lyapunov stability analysis confirming closed-loop stability. AFS is triggered only when yaw-rate deviation exceeds 0.05 rad/s or adhesion utilization reaches 90%, incorporating hysteresis to ensure smooth transitions and minimize unnecessary steering intervention. Comprehensive co-simulations using Carsim and MATLAB/Simulink under diverse failure locations (left-front and right-rear wheels), road adhesion levels (μ = 0.85 and 0.5), and braking intensities (0.2 g–0.6 g) demonstrate that the proposed strategy reduces lateral displacement by up to 85.3% compared to full-time AFS control while maintaining over 99% deceleration satisfaction. The results establish an effective dual-objective fault-tolerant framework that enhances both robustness and functional safety of EMB systems under symmetry-breaking faults, offering a physically interpretable, computationally efficient solution well-suited for real-time automotive applications.

Intelligentization is the development trend of the future automobile industry. Intelligentization requires that the dynamic control of the vehicle can complete the trajectory tracking according to the trajectory output of the decision planning the driving state of the vehicle and ensure the driving safety and stability of the vehicle. However, trajectory limit planning and harsh road conditions caused by emergencies will increase the difficulty of trajectory tracking and stability control of unmanned vehicles. In view of the above problems, this paper studies the trajectory tracking and stability control of distributed drive unmanned vehicles. This paper applies a hierarchical control framework. Firstly, in the upper controller, an adaptive prediction time linear quadratic regulator (APT LQR) path following algorithm is proposed to acquire the desired front-wheel-steering angle considering the dynamic stability performance of the tires. The lateral stability of the DDAUV is determined based on the phase plane, and the sliding surface, in the improved sliding mode control (SMC), is further dynamically adjusted to obtain the desired additional yaw moment for coordinating the path following and lateral stability. Then, in the lower controller, considering the slip and the working load of four tires, a comprehensive cost function is established to reasonably distribute the driving torque of four in-wheel motors (IWMs) for producing the desired additional yaw moment. Finally, the proposed control algorithm is verified by the hardware-in-the-loop (HIL) experiment platform. The results show the path following and lateral stability can be coordinated effectively under different driving conditions.

For autonomous vehicles, high-speed cornering can easily lead to degraded handling stability and increased risks of sideslip or even rollover. Therefore, vehicle phase-plane stability-region analysis has become an important topic in active safety-control research. However, most existing studies still construct phase-plane stability regions mainly based on simplified vehicle models, without sufficiently considering the influence of vertical load transfer during cornering on tire lateral forces and stability boundaries. To address this issue, this paper proposes a hierarchical control strategy based on phase-plane analysis for active inward tilt vehicles. This method adopts a three-degree-of-freedom vehicle dynamics model and a tire model. By carefully comparing the phase-plane stability regions of active inward tilt and passive roll vehicles and by further analyzing the state-trajectory convergence characteristics of active inward tilt vehicles under different longitudinal speeds, front wheel steering angles, and road adhesion coefficients, the effects of active inward tilt on stability-region expansion and vehicle-state convergence are revealed. Subsequently, a hierarchical control strategy is proposed as an integrated solution to improve vehicle handling stability. The upper-level controller dynamically adjusts the reference values and objective weights according to whether the vehicle state is located in the stable, critical, or dangerous region. The lower-level NMPC controller optimizes the front wheel steering angle and active suspension forces to achieve coordinated trajectory tracking and stability control. Double lane-change simulation results show that active inward tilt can improve the left–right vertical load distribution and expand the lateral stability region. Compared with passive roll and conventional active inward tilt control, the proposed strategy reduces the phase-plane state convergence area by 68% and 75%, respectively, thereby improving vehicle handling stability and active safety under extreme conditions.

This paper presents a three‐layer control architecture designed to enhance vehicle lateral stability under uncertain and varying road conditions. The system utilizes output‐feedback‐based finite‐time controllers to track the desired yaw rate and longitudinal slip in the upper and lower layers, respectively. The middle layer incorporates a finite‐time robust dynamic control allocation to distribute longitudinal slips among the tires. This approach effectively handles uncertainties and changing road conditions without the need for direct estimation of unmeasurable variables such as tire‐road friction and sideslip angle. The proposed output feedback control law consists of a stabilizer component to ensure finite‐time stability, a compensator to eliminate the unknown function in the upper and lower layers, and an auxiliary tracking term. Key advantages of the proposed framework include: no requirement for additional sensors, finite‐time convergence, reduced computational complexity compared to optimization‐based methods, and the ability to perform finite‐time stability analysis for the integrated closed‐loop system. The system performance is evaluated using a validated 10‐degree‐of‐freedom vehicle dynamics model and CarSim simulations during a double‐lane change maneuver. Simulation results demonstrate the superiority of the proposed control structure over sliding mode control, offering improved tracking accuracy and robust performance under varying road conditions.

The article presents authors’ recent results on nonlinear lateral stability of rail vehicles in a curved track. The theories of self-exciting vibrations and bifurcation are the key elements here. The general objective is presentation of extended use of the earlier worked out authors’ method to more complex rail vehicle models. Two 4-axle vehicle models were created. The first one represents coach MKIII described with multibody software by the first author. The second one represents coach 127A described with use of engineering multibody software VI-Rail. The models are described, and method of the analysis is shortly reminded. Then, results for both models are presented. They include verification of the limit cycle possible passage from straight track to circular curve and stability maps for regular curves of different radii and straight track. Next influence of selected suspension parameter and wheel–rail coefficient of friction on vehicle stability is shown. The more general objective is the authors’ say in the hot polemics on the advisability of rail vehicle stability analysis in curves and on the advantages of the nonlinear methods of such analysis over the linear ones.

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.

This paper presents a control strategy to enhance the lateral dynamics stability and handling performance of the four-wheel independently actuated (FWIA) electric vehicles (EVs). The vehicle longitudinal velocity uncertainty and controller saturation are considered, a double layers control scheme is adopted. In the upper layer, the homogeneous polynomial parameter-dependent approach is introduced to track the uncertainty problem, and a multi-objective controller is designed to obtain the desired external yaw moment. In the lower layer, an optimal force distribution method with considering the distribution error and tire workload is employed to allocate the desired external yaw moment into forces of the four in-wheel motors. Simulation results verify the effectiveness of the proposed control strategy.

Assessing ankle abductor (ABD) strength is clinically relevant because the peroneal muscles are key contributors to lateral ankle stability. However, no previous study has examined whether isolated ABD strength can be measured reliably and reflects peroneal muscle function. This study evaluated the intra-session test–retest reliability of a novel device for isometric ABD strength assessment and examined construct validity using surface electromyography (EMG). Two independent samples were included: n = 24 for test–retest reliability and n = 15 for EMG construct validity. Strength testing was performed using a custom-built, examiner-independent device that standardized positioning and stabilization while applying resistance through a load cell. Participants executed maximal isometric ABD efforts. Reliability was assessed using ICC (2,1), SEM, coefficient of variation, and minimal detectable change (MDC95) values. EMG activity of the peroneus longus, peroneus brevis, and tibialis anterior was recorded during ankle ABD to examine muscle-specific activation patterns across force levels (30%, 50%, and 70% of maximal voluntary force). The device showed good-to-excellent reliability, with ICC values from 0.82 to 0.94 and acceptable SEM and MDC95. The most stable results were obtained using the highest value across five or six attempts. EMG analyses demonstrated a clear graded, force-dependent activation pattern in the peroneal muscles, whereas tibialis anterior activity showed greater variability and a less consistent response. These findings indicate that the device provides consistent ABD strength measurements and elicits muscle-specific activation patterns consistent with peroneal function, supporting clinical assessment of lateral ankle stabilizers and guiding rehabilitation.

The design and industrial innovation of intelligent agricultural machinery and equipment for saline alkali land are important means for comprehensive management and capacity improvement of saline alkali land. The autonomous and unmanned agricultural tractor is the inevitable trend of the development of intelligent machinery and equipment in saline alkali land. As an underactuated system with non-holonomic constraints, the independent trajectory planning and lateral stability control of the tractor-trailer system (TTS) face challenges in saline alkali land. In this study, based on the nonlinear underactuation characteristics of the TTS and the law of passive trailer steering, a dual-trajectory collaborative control model was designed. By solving the TTS kinematic/dynamic state space, a nonlinear leading system that can generate the reference pose of a tractor-trailer was constructed. Based on the intrinsic property of the lateral deviation of the TTS, a collaborative trajectory prediction algorithm that satisfies the time domain and system constraints is proposed. Combining the dual-trajectory independent offset and lateral stability parameter of the TTS, an energy function optimization control parameter was constructed to balance the system trajectory tracking performance and lateral control stability. The experimental results showed good agreement between the predicted trailer trajectory and the collaborative control trajectory, with an average lateral error not exceeding 0.1 m and an average course angle error not exceeding 0.054 rad. This ensures that the dynamic controller designed around the tractor-trailer underactuation system can guarantee the smoothness of the trailer trajectory and the controlling stability of the tractor in saline alkali land.