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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.

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.

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.

The current vehicle stability judgment methods in the stability control of distributed drive electric vehicles focuses on using the phase plane method, but the existing phase plane stability boundary delineation methods ignore the influence of the front wheel rotation angle. Therefore, a new phase plane partitioning method (NPPPM) is proposed in this paper, and a new phase plane stability domain library is established. Based on this, the direct yaw moment control of distributed drive electric vehicle through phase plane analysis is established. Firstly, the influence of the front wheel turning angle on the phase plane is considered in the design of the phase plane stability boundary, and a new stability boundary function is fitted by the least squares method. This approach avoids the lateral instability caused by too large a front-wheel turning angle and improves the accuracy of vehicle stability judgment. Next, a hierarchical direct transverse moment controller is constructed using Matlab/Simulink. Among them, the upper layer controller adopts sliding mode control with the sideslip angle error of the center of mass as the tracking target. The lower controller adopts the optimal configuration algorithm with the optimal road adhesion utilization as the objective function. Finally, the effectiveness and superiority of the proposed phase plane partitioning method (NPPPM) for distributed drive electric vehicles with sinusoidal and angular order inputs for the front wheel rotation angle are verified by simulation.

Currently, one of the most important challenges facing autonomous vehicles’ development due to varying driving conditions is effective path tracking while considering lateral stability. To address this issue, this study proposes the optimization of the linear quadratic regulator (LQR) control system by using the genetic algorithm (GA) to support the vehicle in following the predefined path accurately, minimizing the sideslip, and stabilizing the vehicle’s yaw rate. The dynamic system model of the vehicle is represented based on yaw rate angle, lateral speed, and vehicle sideslip angle as the variables of the state space model, with the steering angle as an input parameter. Using the GA to optimize the LQR control by tuning the weighting of the Q and R matrices led to enhancing the system response and minimizing deviation errors via a proposed cost function of GA. The simulation results were obtained using MATLAB/Simulink 2024a, with a representation of a predefined path as a Gaussian path. Under external and internal disturbances, such as road conditions, lateral wind, and actuator delay, the model demonstrates improved tracking performance and reduced sideslip angle and lateral acceleration by adjusting the longitudinal vehicle speed. This work highlights the effectiveness of robust control in addressing path planning, driving stability, and safety in autonomous vehicle systems.

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.

This paper presents a comparison among path tracking controllers on low-friction roads for autonomous vehicles. There are two goals in this paper. The first is to check the performance of path tracking controllers on low-friction roads, and the second is to check the effectiveness of four-wheel steering (4WS) for path tracking. To fully investigate the performance of path-tracking controllers on low-friction roads in this paper, the pure pursuit method, Stanley method, PID control, linear quadratic regulator, sliding mode control and model predictive control are designed and compared in terms of some measures. Front and four-wheel steering are adopted as actuators for path tracking. To utilize 4WS in the pure pursuit method, Stanley method and PID control, a yaw rate tracking control is adopted. With the designed path tracking controllers, a simulation is conducted on vehicle simulation software. From the simulation results, it is shown that most path tracking controllers are effective for path tracking on low-friction roads if finely tuned, and that 4WS is not recommended for path tracking on low-friction roads.

For distributed drive autonomous vehicles, adding lateral stability control (LSC) to the trajectory tracking control (TTC) can optimize the distribution of the driving torque of each wheel, so that the vehicle can track the planned trajectory while maintaining stable lateral motion. However, the influence of adding LSC on the TTC system is still unclear. Firstly, a stability-track hierarchical control structure composed of LSC and TTC was established, and the interaction between the two layers was identified as the key of this paper. Then, the Intrinsic Mechanistic framework of the stability-tracking control (STC) was proposed by establishing and analyzing the vehicle dynamic model and control process of two layers. Finally, through simulation experiments, it was found that the change in the curvature of the target trajectory will make the tracking target trajectory and maintaining the lateral stability of the vehicle appear to conflict; in addition, in the LSC layer, the steering characteristics and delay characteristics of different reference models have a greater impact on the lateral stability and trajectory tracking performance; moreover, adjusting the preview time has a more obvious effect on trajectory tracking and lateral stability than the stability correction intensity coefficient.

A Stackelberg game-based cooperative control strategy is proposed for enhancing the lateral stability of a four-wheel independently driving electric vehicle (FWID-EV). An upper‒lower double-layer hierarchical control structure is adopted for the design of a stability control strategy. The leader‒follower-based Stackelberg game theory (SGT) is introduced to model the interaction between two unequal active chassis control subsystems in the upper layer. In this model, the direct yaw-moment control (DYC) and the active four-wheel steering (AFWS) are treated as the leader and the follower, respectively, based on their natural characteristics. Then, in order to guarantee the efficiency and convergence of the proposed control strategy, a sequential quadratic programming (SQP) algorithm is employed to solve the task allocation problem among the distributed actuators in the lower layer. Also, a double-mode adaptive weight (DMAW)- adjusting mechanism is designed, considering the negative effect of DYC. The results of cosimulation with CarSim and Matlab/Simulink demonstrate that the proposed control strategy can effectively improve the lateral stability by properly coordinating the actions of AFWS and DYC.

The vehicle lateral stability control algorithm is an essential component of the electronic stability program (ESP), and its control effect directly affects the vehicle’s driving safety. However, there are still numerous shortcomings and challenges that need to be addressed, including enhancing the efficiency of processing intricate pavement condition data, improving the accuracy of parameter adjustment, and identifying subtle and elusive patterns amidst noisy and ambiguous data. The introduction of machine learning algorithms can address the aforementioned issues, making it imperative to apply machine learning to the research of lateral stability control algorithms. This paper presented a vehicle lateral electronic stability control algorithm based on the back propagation (BP) neural network and PID control algorithm. Firstly, the dynamics of the whole vehicle have been analytically modeled. Then, a 2 DOF prediction model and a 14 DOF simulation model were built in MATLAB Simulink to simulate the data of the electronic control units (ECU) in ESP and estimate the dynamic performance of the real vehicle. In addition, the self-correction of the PID algorithm was verified by a Simulink/CarSim combined simulation. The improvement of the BP neural network to the traditional PID algorithm was also analyzed in Simulink. These simulation results show the self-correction of the PID algorithm on the lateral stability control of the vehicle under different road conditions and at different vehicle speeds. The BP neural network smoothed the vehicle trajectory controlled by traditional PID and improved the self-correction ability of the control system by iterative training. Furthermore, it shows that the algorithm can automatically tune the control parameters and optimize the control process of the lateral electronic stability control algorithm, thus improving vehicle stability and adapting it to many different vehicle models and road conditions. Therefore, the algorithm has a high practical value and provides a feasible idea for developing a more intelligent and general vehicle lateral electronic stability system.