Search Results (70)

In this study, we propose a novel asymmetric high-performance MEMS pendulum accelerometer comprising a sensitive structure and an interface circuit. The sensitive structure, designed with asymmetric mass blocks, significantly improves both sensitivity and structural stability. The sensor is fabricated using a double-side polished (100) N-type silicon wafer and its structure is ultimately realized through ICP (Inductively Coupled Plasma) etching. We also develop and fabricate the corresponding interface circuit. The accelerometer is evaluated through a static field roll-over test, demonstrating excellent performance with a sensitivity of 1.247 V/g and a nonlinearity of 0.8% within the measurement range of −2 g to 2 g. Full article

Despite significant advancements in roll stability control for individual vehicle types, comparative research across on-road and off-road vehicles remains limited, hindering cross-disciplinary innovation. This study bridges this gap by systematically analyzing roll stability control in both vehicle categories, focusing on theoretical foundations, key technologies, and experimental validation methods. On-road vehicles rely on mature technologies like active suspension, braking, and steering, which enhance safety through sensor monitoring, rollover prediction, and integrated stability control. Validation is primarily performed through hardware-in-the-loop simulations and on-road testing. Off-road vehicles, operating in more complex environments with dynamic load changes and rugged terrain, emphasize adaptive leveling, direct torque control, and active steering. Their stability control strategies must also account for terrain irregularities, real-time load shifts, and extreme slopes, validated through scaled-model tests and field trials. Comparative analysis reveals that while both vehicle types face similar challenges, their control strategies differ significantly: on-road vehicles focus on handling and high-speed stability, while off-road vehicles require more robust, adaptive mechanisms to manage environmental uncertainties. Future research should explore multi-system collaborative control, such as integrating active suspension with intelligent terrain perception, to improve adaptability and robustness across both vehicle categories. Furthermore, the integration of machine learning and advanced predictive algorithms promises to enhance the intelligence and versatility of roll stability control systems. Full article

In response to the 2023 mandate requiring electronic stability control (ESC) for trucks in South Korea, domestic manufacturers have called for a relaxation of the maximum safe slope angle to reduce production costs. However, limited research exists on the quantitative relationship between ESC implementation and vehicle rollover stability under relaxed safety standards. This study addresses this gap by conducting dynamic simulations of standardized rollover tests to evaluate the static stability factor (SSF) and by developing a machine-learning-based model for predicting rollover risk. The model incorporates planned path curvature and driving speed to compute lateral acceleration, which serves as a key input for predicting the lateral load transfer ratio (LTR), a critical indicator of vehicle stability. Among several models tested, the recurrent neural network (RNN) achieved the highest accuracy in LTR prediction. The results highlight the effectiveness of integrating data-driven models into dynamic stability assessment frameworks, offering practical insights for optimizing route planning and speed control—particularly in autonomous freight vehicle applications. Full article

Electric vertical take-off and landing (eVTOL) aircraft are transforming urban air mobility (UAM) by providing efficient, low-emission, and rapid transit in congested cities. However, ensuring safe and stable landings remains a critical challenge, particularly in constrained urban environments with variable wind conditions. This study investigates the landing aerodynamics of a multi-rotor eVTOL using the lattice Boltzmann method (LBM), a computational approach well-suited to complex boundary conditions and parallel processing. This analysis examines the ground effect, descent speed, and crosswind influence on lift distribution and stability. A rooftop landing scenario is also explored, where half of the rotors operate over a rooftop while the rest remain suspended in open air. Results indicate that rooftop landings introduce asymmetric lift distribution due to crosswind and roof-induced flow circulation, significantly increasing rolling moment compared to ground landings. These findings underscore the role of descent speed, crosswinds, and landing surface geometry in eVTOL aerodynamics, particularly the heightened risk of rollover in rooftop scenarios. Full article

Considering the influence of high-speed obstacle avoidance trajectory in the optimization design stage of intelligent bus aerodynamic shape. A collaborative optimization method aiming at aerodynamic structure and trajectory control system for intelligent bus rollover stability is proposed to reduce the interference of lateral aerodynamic load caused by large bus side area on driving stability and improve the rollover safety of intelligent bus in high-speed obstacle avoidance process. At the conceptual design stage, a multidisciplinary co-design optimization frame of aerodynamics/dynamics/control is built, and an adaptive Gaussian Process Regression approximate modeling method is proposed to establish an approximate model of high-precision and high-efficiency rollover evaluation index with rollover stability as the optimization objective and obstacle avoidance safety and resistance to crosswind interference as constraints. Taking rollover stability and obstacle avoidance safety as the optimization objectives, the integrated design of static structural parameters and dynamic control parameters of intelligent buses is carried out. The results show that the proposed MDO method can obtain the aerodynamic shape of the vehicle body with low crosswind sensitivity and a safe and stable obstacle avoidance trajectory. Compared with the initial trajectory, the peak lateral load transfer rate during the obstacle avoidance process decreases by 33.91%, which significantly reduces the risk of rollover. Compared with the traditional serial optimization method, the proposed co-design optimization method has obvious advantages and can further improve the driving safety performance of intelligent buses. Full article

Path planning for intelligent semi-trailers encounters numerous challenges in complex traffic conditions. Serious consequences, such as vehicle rollover, may occur when the traffic conditions change. Therefore, it is vital to consider both the surrounding dynamic traffic conditions and the vehicle’s roll stability during the lane-changing process of intelligent semi-trailers. We propose an innovative path-planning method tailored for intelligent semi-trailers. This path-planning method is designed for semi-trailers on straight-road alignments. Firstly, we employ a fuzzy inference system to process information about surrounding traffic, make lane-changing decisions, and determine the starting point. Secondly, the lane-changing path is generated using a B-spline curve. Subsequently, we apply a particle swarm optimization algorithm to enhance the B-spline curve. Thirdly, we utilize a Transformer model to analyze the nonlinear relationships among information about surrounding traffic, vehicle information, and the roll stability of the intelligent semi-trailer. We establish the roll stability boundary for the vehicle. Finally, we design a multi-objective cost function to select the optimal path. The simulation results demonstrate that the proposed method dynamically adapts the planned path to variations in driving parameters, ensuring trackability while reducing the steering angle, lateral acceleration, and yaw rate. This approach meets the roll stability requirements of intelligent semi-trailers, significantly enhances their stability during lane changing, and provides robust support for safe and efficient operation. Full article

In order to solve the problem that electric forklifts are prone to rollover when turning, a coordinated control strategy for anti-rollover of electric forklifts is proposed. A forklift dynamics simulation model with integrated centroid position is constructed, the stability of the forklift is judged by the phase plane area division method, the upper controller, including the active steering controller, and the differential brake controller are designed, the control weight coefficient of the active steering controller and the differential brake controller in different control domains is determined through the coordination controller, so as to obtain the required additional rear wheel rotation angle and additional yaw torque, and the braking force distribution controller exerts braking force to the wheel according to the additional yaw torque. A simulation model is built to verify the effectiveness of this control strategy, and the simulation results show that the control strategy can greatly reduce the risk of rollover when the forklift is cornering and further improve the stability of the forklift. Full article

In order to coordinate the transverse motion control and longitudinal motion control in the tracking control process and ensure the yaw stability and roll stability in the tracking process, a transverse and longitudinal coordinated control method of three-axis heavy vehicles is designed based on model predictive control. The lateral motion controller is designed based on the phase plane method. The upper controller calculates the front wheel angle and additional yaw moment, which ensures the yaw stability while tracking the vehicle. The lower controller calculates the driving force and braking force of the three-axis heavy vehicle. The velocity planning method is designed with the coupling point of longitudinal velocity to coordinate the lateral and longitudinal motion controllers and prevent vehicle rollover. By building the vehicle model in Trucksim (2016.1) and establishing the horizontal and vertical coordination control in Matlab (R2016b), the designed horizontal and vertical coordination control method is simulated and verified. The simulation results show that the designed method can accurately track the reference trajectory while ensuring the yaw stability and roll stability of the three-axis heavy vehicle. Full article

This paper addresses the significant variations in model parameters observed in autonomous commercial vehicles in comparison to passenger cars, with a disparity noted largely due to changes in load. Additionally, it tackles the issue of path tracking inaccuracy caused by external factors such as delays in steering system execution. The proposed solution is a hierarchical control method, grounded in mass estimation and model predictive control(MPC). Initially, to counter the variation in model parameters, a mass estimator is developed. This estimator utilizes the recursive least squares method with a forgetting factor, coupled with M-estimation, thereby enhancing the robustness of the estimation and achieving model correction. Subsequently, an upper-level MPC controller is constructed based on the error model, thereby augmenting the precision of tracking control. To address the delay in the steering system execution common in autonomous commercial vehicles, a lower-level steering angle compensator is designed to expedite the response speed of the execution. The feasibility of the vehicle’s front wheel angle is constrained via the rollover index, thereby enhancing vehicle stability during operation. The efficacy of the proposed control strategy is demonstrated with joint simulations using TruckSim/Simulink and real vehicle tests. The results indicate that this strategy can effectively manage the model mismatch caused by load changes in commercial vehicles and the delay in steering system execution, thereby exhibiting commendable tracking accuracy, adaptability, and driving stability. Full article

In off-road environments, the lateral rollover stability of articulated unmanned rollers (URs) is critical to ensure operational safety and efficiency. This paper introduces the concept of a rollover energy barrier (REB), a symmetry-based metric that quantifies the energy margin between the current state and the critical rollover threshold of articulated rollers. URs exhibit dynamic asymmetry due to their hydraulic steering systems, which differ significantly from traditional passenger vehicles. To address these challenges, we propose a hierarchical control framework inspired by the principles of dynamic symmetry. This framework integrates Nonlinear Model Predictive Control (NMPC) and Active Disturbance Rejection Control (ADRC): NMPC is used for trajectory planning by incorporating the REB into the cost function, ensuring rollover stability, while ADRC compensates for dynamic asymmetries, model uncertainties, and external disturbances during trajectory tracking. Simulation and experimental results validate the effectiveness of the proposed control strategy in enhancing the rollover stability and tracking performance of the URs under off-road conditions. Full article

Commercial vehicles frequently experience lateral interferences, such as crosswinds or side slopes, during extreme maneuvers like emergency steering and high-speed driving due to their high centroid. These interferences reduce vehicle stability and increase the risk of rollover. Therefore, this study takes a bus as the carrier and designs an anti-rollover control strategy based on mixed-sensitivity and robust $H_{\infty }$ controller. Specifically, a 7-DOF vehicle dynamics model is introduced, and the factors influencing vehicle rollover are analyzed. Based on this, to minimize excessive intervention in the vehicle’s dynamic characteristics, the lateral velocity, roll angle, and roll rate are recorded at the vehicle’s rollover threshold as desired values. The lateral load transfer rate (LTR) is chosen as the evaluation index, and the required additional yaw moment is determined and distributed to the wheels for anti-rollover control. Furthermore, to verify the effectiveness of the proposed anti-rollover control strategy, a co-simulation platform based on MATLAB/Simulink and TruckSim is developed. Various dynamic lateral interferences (side winds with different changing trends and wind speeds) are introduced, and the fishhook and J-turn maneuvers are selected to analyze and compare the proposed control strategy with a fuzzy logic algorithm. The results indicate that the maximum LTR of the vehicle is reduced by 0.11. Additionally, the lateral acceleration and yaw rate in the steady state are reduced by more than 1.8 m/s² and 15°, respectively, enhancing the vehicle’s lateral stability. Full article

In complex terrain, such as uneven roads or irregular terrain, two-wheeled heavy-duty self-balancing vehicles are easily affected by external interference factors, causing rollover or rendering the vehicle unable to move, which poses a greater challenge to its stability control. Therefore, it is necessary to establish a kinematic model of a two-wheeled vehicle and design a control system to study its driving stability. This paper aims to study the stability control system of a two-wheeled self-balancing vehicle under complex terrain. First, a self-balancing vehicle modeling method based on complex terrain is designed. By analyzing the motion characteristics of the self-balancing vehicle, a kinematic model suitable for complex terrain is established, which provides a basis for subsequent control algorithms. Secondly, a precise control system is designed for different terrain conditions, and parameters such as vehicle attitude, speed and acceleration are adjusted through the Proportional–Integral–Derivative (PID) control algorithm to achieve the smooth operation of the self-balancing vehicle in complex terrain. In addition, a vehicle-mounted camera is used to capture terrain images in real time, and different terrains are accurately identified through the terrain recognition algorithm based on deep learning, thereby determining the friction coefficient and effectively improving the stability of the self-balancing vehicle on complex terrain. The experimental results show that the designed control system can enable the self-balancing two-wheeled vehicle to achieve stable balance control in different terrains, and has good applicability and stability. Full article

Considering the requirements pertaining to the trafficability of off-road vehicles on rough roads, and since their roll stability deteriorates rapidly when turning violently or passing slant roads due to a high center of gravity (CG), an efficient anti-slip control (ASC) method with superior instantaneity and robustness, in conjunction with a rollover prevention algorithm, was proposed in this study. A nonlinear 14 DOF vehicle model was initially constructed in order to explain the dynamic coupling mechanism among the lateral motion, yaw motion and roll motion of vehicles. To acquire physical state changes and friction forces of the tires in real time, corrected LuGre tire models were utilized with the aid of resolvers and inertial sensors, and an adaptive sliding mode controller (ASMC) was designed to suppress each wheel’s slip ratio. In addition, a model predictive controller (MPC) was established to forecast rollover risk and roll moment in reaction to the change in the lateral forces as well as the different ground heights of the opposite wheels. During experimentation, the mutations of tire adhesion capacity were quickly discerned and the wheel-hub drive motors (WHDM) and ASC maintained the drive efficiency under different adhesion conditions. Finally, a hardware-in-the-loop (HIL) platform made up of the vehicle dynamic model in the dSPACE software, semi-active suspension (SAS), a vehicle control unit (VCU) and driver simulator was constructed, where the prediction and moving optimization of MPC was found to enhance roll stability effectively by reducing the length of roll arm when necessary. Full article

The automotive industries currently face challenges such as emission limits, traffic congestion, and limited parking, which have prompted shifts in consumer preferences and modern passenger vehicle requirements towards compact vehicles. However, given the inherent limited width of compact vehicles, the potential risk of vehicle rollover is greater than that of regular vehicles. This paper addresses the safety concerns associated with vehicle rollover, focusing on narrow tilting vehicles (NTVs). Quantifying stability involves numerical indicators such as the lateral load transfer ratio (LTR). Additionally, a unique approach is taken by applying ZMP (zero moment point), commonly used in the robotics field, as an indicator of vehicle stability. Effective roll control requires a detailed analysis of the vehicle’s characteristic model and the derivation of lateral and roll dynamics. The paper presents the detailed roll dynamics of an NTV with a MacPherson strut-type suspension. A stability-enhancing method is proposed using a cascade structure based on the internal robust position controller and outer roll stability controller, addressing challenges posed by disturbances. Experimental verification using Simscape Multibody and CarSim validates the dynamic model and controller’s effectiveness, ensuring the reliability of the proposed tilting control for NTVs in practical scenarios. Full article

Although the probability of a rollover accident is lower than that of other forms of collision, rollover is a serious accident that can break the symmetry of the vehicle and cause serious loss of life and property. There are many factors affecting rollovers, such as the environment, the vehicle, and the driving control. A coach comprises a complex dynamic system; as such, the accuracy and rationality of the used mathematical model are decisive in the study of coach rollover warning and control. By analogy with the modeling method of an automobile collision accident, the general process of a coach rollover accident is analyzed in this study in combination with the contact form and freedom of motion characteristic of the coach body and external environment. According to the principle of conservation of energy, the mathematical models of critical rollover impact force in a collision between vehicles and obstacles and in a collision between two vehicles are established, allowing for analysis of the relationships between the critical tripped rollover impact forces required for a 90° rollover and the continuous action time and collision point height. During the collision between the vehicle and the obstacle, the occurrence of a vehicle rollover is related not only to the impact force in the collision process but also to the collision duration time. Even if the impact force is relatively small, the collision lasts long enough that a second collision may occur until the vehicle rolls over. In the process of a two-vehicle collision, the critical rollover impact force is not only related to the vehicle mass but also to the vehicle wheelbase and the height of the collision point. Based on the law of conservation of momentum, the mathematic models of 90-degree rollover and 180-degree rollover are established, and the critical rollover velocities are calculated. The purpose of this study is to provide reference and guidance for the research methods of vehicle rollover stability and anti-rollover control in the intelligent vehicle era. Full article