Explore the vast range of titles available.

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 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.

Currently, intensive research is being carried out to improve the operational characteristics of the car: vibration protection, smoothness, stability, and controllability. These properties are largely determined by the characteristics of the vehicle suspension, which provides a connection between the carrier system and the wheels of the vehicle. Significant attention is paid to the development of active suspensions, in which additional actuators are used to form the necessary characteristics, in particular, linear dc motors. The use of active actuators permits to control the position of the car body, including its lateral roll. In the article, relations are obtained that establish the dependence of additional elastic deformations in the suspension and the car roll angle on the centrifugal force in a stationary mode. When developing a linearized mathematical model of the control object for the study of nonstationary modes, a two-mass design scheme is used and operator equations are obtained that take into account the elastic–dissipative properties of the sprung and unsprung parts of the car, as well as an additional control action created by the actuator. It is shown that the dynamic properties of the studied control object can be approximately described by the transfer functions of a second-order aperiodic link or an oscillatory link. For the former case, a single-loop system was developed, which was closed in terms of the roll angle with a proportional-integral-derivative (PID) controller. In the latter situation, it is advisable to use a two-loop system with an internal flexible feedback loop for suspension deformation and an external loop closed for the roll angle using a PID controller. The possibility of forming a feedback signal in the strain rate of the suspension in the internal loop with the help of an EMF sensor of a linear dc motor is demonstrated. On the basis of the block diagram, a computer model of the system is developed, and for typical parameters of the control object, a study is made of transient processes of working off a disturbance in the form of a change in centrifugal force. Based on the simulation results, it was found that the use of the developed automatic control system (ACS) provides high accuracy in stabilizing the vehicle roll angle.

This paper introduces a hierarchical three-layer adaptive control structure aimed at improving vehicle lateral stability under parametric uncertainties and unknown road conditions. The primary innovation is the finite-time smooth adaptive control algorithm, which is applied across the upper and lower control layers, as well as in the middle layer’s dynamic control allocation. In the upper layer, a novel smooth adaptive finite-time sliding mode controller is implemented using an integral fixed-time sliding variable to track the desired yaw rate. Similarly, the lower layer employs the same methodology to track the desired longitudinal slip. Additionally, the middle layer incorporates a finite-time smooth dynamic control allocation method to optimally distribute longitudinal slip among the tires, eliminating the need for tire-road friction estimation. The proposed framework offers several advantages, including reduced computational complexity, practical constraints handling and improved adaptability to varying road conditions and uncertainties. Simulations conducted on a validated 10-degree-of-freedom (10-DOF) vehicle model demonstrate the framework's superior performance in terms of tracking accuracy, smooth control signals, finite-time stability and adaptability to diverse conditions when compared to conventional methods.

PurposeThis paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.Design/methodology/approachFirstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.FindingsThe simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.Originality/valueThe MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.