Vehicle Stability Research Articles

Research on the application of automatic control theory in automatic driving technology

Autonomous driving technology has been widely used and studied in depth in recent years, and has become a hot field in the automotive industry. As one of the core methods to realize automatic driving, automatic control theory provides an important theoretical basis and practical guidance for automatic driving systems. This paper aims to explore the application of automatic control theory to autonomous driving and analyze its potential impact on improving driving safety, comfort and efficiency. This paper first introduces the basic principles and components of the autonomous driving system. Among them, automatic control theory plays an important role in decision-making and execution. Secondly, this paper discusses the specific application of automatic control theory in automatic driving. These include PID controller-based vehicle stability control, model predictive control (MPC) for path planning and trajectory tracking. These applications enable autonomous driving systems to respond in real time to environmental changes and maintain vehicle stability and safety. Finally, the paper discusses the challenges and future directions of automatic control theory in autonomous driving. Future research should focus on further improving the robustness and adaptability of automatic control algorithms to cope with complex driving scenarios and uncertainties. To sum up, automatic control theory plays an important role in automatic driving and has broad application prospects. Through continuous improvement and innovation, automatic control theory will make an important contribution to the realization of safer, more efficient, and more intelligent autonomous driving technology.

Nonlinear Robust Control of Vehicle Stabilization System with Uncertainty Based on Neural Network

To effectively suppress the effects of uncertainties including unmodeled dynamics and external disturbances in the vehicle stabilization system, a nonlinear robust control strategy based on a multilayer neural network is proposed in this paper. First, the mechanical and electrical coupling dynamics model of the vehicle stabilization system, considering model uncertainty and actuator dynamics, is refined. Second, the lumped uncertainty of the vehicle stabilization system is estimated by a multi-layer neural network and compensated by feedforward control. The high robustness of the system is ensured by constructing the sliding mode feedback control law. The proposed control method overcomes the limitations of sliding mode technology and the neural network and is naturally applied to the vehicle stabilization system, avoiding the adverse effects of high-gain feedback. Based on Lyapunov theory, it is demonstrated that the proposed controller is able to achieve the desired stability tracking performance. Finally, the effectiveness of the proposed control strategy is verified by co-simulation and comparative experiments.

Research on lateral stability control strategy for distributed drive electric vehicles considering driving style

To improve the handling stability of distributed drive electric vehicle (DDEV), a lateral stability control strategy considering driving style is proposed. Firstly, a driving style recognition model based on support vector machines (SVM) was constructed to identify driving styles in different lateral motion scenarios. Then, a layered architecture is used to design a driver/Active Front Steering (AFS)/Direct Yaw Control (DYC) stability coordination control strategy. The upper layer is based on the phase plane of the sideslip angle and extension control theory, dividing the vehicle operating range into classical, extension, and non domains. In the middle level, three control strategies are designed for different work domains. In the classical domain, a driver/AFS coordinated control strategy based on non-cooperative Nash games is constructed, and the weight coefficients in the AFS system cost function are modified based on driving characteristics; Construct a coordinated control strategy for AFS/DYC systems based on cooperative Pareto games in the extension domain; In non-domain, construct a direct yaw torque control strategy based on twin-delayed deep deterministic policy gradient (TD3) reinforcement learning. The lower layer aims to minimize tire utilization and optimizes the allocation of additional yaw moment. Finally, by establishing Hardware in the Loop (HIL) experiment platform, the effectiveness of the proposed control strategy is verified in lane changing scenarios of different styles of drivers. Compared to DYC stability control based solely on TD3, the maximum yaw rate and standard deviation of the lateral stability control strategy considering driving style reduce by 2.4 % and 10.82 %, respectively, and the maximum and standard deviation of the sideslip angle reduce by 29.29 % and 39.24 %, respectively, thereby improving the vehicle's handling stability.

A new formulation of the understeer gradient and stability margin to analyse vehicle steady-state cornering behaviour

The understeer gradient is a measure that describes a vehicle's cornering behaviour as either understeer, neutral, or oversteer. Several formulations of this measure have been developed, all of which are reviewed in this paper. It has been found that all of these formulations rely essentially on the lateral acceleration of the vehicle. While the understeer gradient provides a useful connection to vehicle stability, its reliance on lateral acceleration can result in numerical instability and error at high levels of lateral acceleration. To address this issue, a new formulation of the understeer gradient is proposed in this paper. This new formulation is based on vehicle speed and is suitable for all maneuvers. Unlike previous formulations, it does not rely on lateral acceleration or provide a direct connection to vehicle stability. To address the issue of vehicle stability, a new measure called the stability margin is also proposed in this paper. The stability margin provides a more precise measurement of a vehicle's stability than the understeer gradient alone. By combining the stability margin with the new formulation of the understeer gradient, a more comprehensive picture of a vehicle's cornering behaviour and stability can be obtained.

Reducing Tyre Wear Emissions of Automated Articulated Vehicles through Trajectory Planning.

Effective emission control technologies and eco-friendly propulsion systems have been developed to decrease exhaust particle emissions. However, more work must be conducted on non-exhaust traffic-related sources such as tyre wear. The advent of automated vehicles (AVs) enables researchers and automotive manufacturers to consider ways to further decrease tyre wear, as vehicles will be controlled by the system rather than by the driver. In this direction, this work presents the formulation of an optimal control problem for the trajectory optimisation of automated articulated vehicles for tyre wear minimisation. The optimum velocity profile is sought for a predefined road path from a specific starting point to a final one to minimise tyre wear in fixed time cases. Specific boundaries and constraints are applied to the problem to ensure the vehicle's stability and the feasibility of the solution. According to the results, a small increase in the journey time leads to a significant decrease in the mass loss due to tyre wear. The employment of articulated vehicles with low powertrain capabilities leads to greater tyre wear, while excessive increases in powertrain capabilities are not required. The conclusions pave the way for AV researchers and manufacturers to consider tyre wear in their control modules and come closer to the zero-emission goal.

Research on layered control of path tracking for unmanned industrial vehicles based on fully hydraulic steering leakage compensation

Industrial vehicles work in complex terrain, the variable centre of mass position, leakage nonlinearity of full hydraulic steering and other issues lead to poor path tracking stability and accuracy. In this paper, an intelligent hierarchical controller for industrial vehicles’ path tracking is designed with full hydraulic steering leakage compensation, including an upper decision layer and a lower execution layer. The upper decision layer observes the pavement-tyre adhesion coefficients through an extended Kalman filter algorithm, and uses the real-time observations of the adhesion coefficients to establish a variable constraint control for the linear time-varying MPC, and thus performs the path tracking control. The lower actuator layer receives the upper layer’s target steering wheel angle output and performs steering operations. Based on the establishment of a mathematical model of full hydraulic steering considering the leakage characteristics, it analyses the leakage disturbance factors and constructs a fuzzy feed-forward steering leakage compensation controller to compensate for the leakage disturbances in real time for path tracking steering. Simulation and experimental results show that the lateral acceleration, sideslip angle, tyre side slip angle and tracking error of intelligent industrial vehicles under different loads and working conditions are improved by more than 32.4%, 35.8%, 40.2% and 45.8% respectively, and the designed hierarchical controller for the path tracking of intelligent industrial vehicles, which considers the compensation of all-hydraulic steering leakage, can effectively improve the path tracking stability and tracking accuracy of intelligent industrial vehicles under different loads and working conditions.

A design an optimized fuzzy adaptive proportional-integral-derivative controller for anti-lock braking systems

This paper introduces a new control theory for optimizing anti-lock braking systems (ABS) in automotive applications. Anti-lock systems play a critical role in ensuring vehicle safety during braking by preventing wheel lock-up. However, conventional ABS control algorithms often struggle to adapt to changing road conditions and vehicle dynamics. The error tuning mechanism was not perfect because the occurrence of error is unique based on each application. In response to this challenge, we propose a new approach that combines fuzzy logic and adaptive proportional-integral-derivative (PID) control. The controller more robust to noise and uncertainties in the system. The present research work has concentrated on designing a novel Wavelet Fuzzy adaptive-hybrid Lion-strawberry Proportional-Integral-Derivative (WFA-HLSPID) for the ABS to maximize the control performance while it applied in the vehicle. Simulation results and comparative analysis demonstrate the superior performance of the proposed control theory when compared to traditional ABS systems. The optimized fuzzy adaptive PID control not only enhances vehicle stability during braking but also improves stopping distance. In all cases, the stopping distance, slip rate, and friction have been validated using MATLAB/Simulink.

Path-following control of 4WIS/4WID autonomous vehicles considering vehicle stability based on phase plane

In order to ensure the following accuracy and improve the operational stability of four-wheel independent driving and four-wheel independent steering autonomous vehicles, this paper proposes a path-following control strategy based on the β−[Formula: see text] phase plane. First, based on the kinematic relationship between the vehicle and the reference path, the linear matrix inequality theory is used to design the H∞ controller to obtain the wheel steering angle. Then, the vehicle steering system is subjected to nonlinear analysis according to phase plane theory, and a partition region controller is designed. In the unstable region, the instability degree of the vehicle is predicted by quadratic polynomial extrapolation and the particle swarm optimization PID controller is designed to determine the required yaw moment to restore the vehicle to the stable region. In the stable region, a fuzzy sliding mode controller is adopted to determine the required yaw moment so that the actual state variable of the vehicle follows the ideal state variable. Finally, the optimal tire force distributor is designed such that the required forces are allocated to all four wheels. The simulation results show that the proposed method can obtain excellent path-following performance and stability performance under different driving conditions.

Enhanced thermal stability and power factor in layered Bi2Se0.5Te2.5 Thin films across a broad temperature range

Bi2Te3-based n-type thin films with high thermoelectric performances are typically synthesised through the formation of dense structures, a process conventionally conducted at high temperatures. However, this method compromises thermal stability. In this study, structural design and multiple-step annealing were employed to enhance the thermal stability of high-performance Bi2Se0.5Te2.5 thin films. Post-annealing, the disparity in the values of the Seebeck coefficient over the entire test temperature range, was reduced to 20 μV K−1, which is approximately 32 % compared to pre-annealing measurements. The layered Bi2Se0.5Te2.5 thin film demonstrated an elevated power factor (28 × 10−4 W m−1K−2), which was maintained across a broad temperature spectrum. This multiple-step annealing approach not only refines the structural integrity by extending the atomic diffusion duration but also mitigates defects and augments compositional uniformity at reduced temperatures. Consequently, layered Bi2Se0.5Te2.5 films emerge as promising candidates for thermoelectric materials, offering enhanced long-term stability for diverse applications, including electric and hybrid electric vehicles, and portable electronic devices.

Optimization Design of Automotive Rear Tails for Enhancing the Downforce and Grip Effect during High Speed

Vehicle motion control and safety have always been key research directions in the field of automotive engineering. However, the traditional rear tail design has limitations in providing downforce, which cannot adequately meet the needs of high-speed driving. In this study, a computational fluid dynamics (CFD) simulation technique combined with an optimization algorithm was used to systematically investigate the influence of rear tail shape and layout on vehicle downforce. By introducing a parametric rear fin design into the CFD model and applying a multi-objective optimization algorithm, various design options can be systematically explored to improve the downforce performance. It was found that with the optimized tail fin design, the car was able to achieve higher downforce and provide better grip and stability during high speed. Specifically, downforce gains of up to 20% were observed, leading to significant improvements in vehicle handling and stability compared to conventional designs. The results of this research are not only of great significance to the field of automotive engineering but also of substantial help to improve the motion performance and safety of vehicles. optimizing the rear tail design can effectively improve the grip of the car in the process of high speed, provide a safer and more stable driving experience for the driver, and is also expected to play an important role in motorsport and other fields.

Distributed-Drive Vehicle Lateral-Stability Coordinated Control Based on Phase-Plane Stability Region

The lateral stability control of vehicles is one of the most crucial aspects of vehicle safety. This article introduces a coordinated-control strategy designed to enhance the handling stability of distributed-drive electric vehicles. The upper controller uses active front steering and direct yaw moment-control controllers designed based on sliding-mode control theory. The lower controller optimally allocates control inputs to the upper controller, considering factors such as load transfer and tire load rate. It divides the stability region by relying on the phase plane and develops a coordinated-control strategy based on the degree of deviation of the vehicle state from the stability region. The results of the simulation experiments demonstrate that the proposed control strategy effectively improves handling stability under extreme working conditions.

Structural Analysis of an Off-Road Vehicle Suspension System Using CATIA and ANSYS

This project delves into the structural analysis of an off-road vehicle suspension system, employing cutting-edge software tools such as CATIA and ANSYS. Off-road vehicles necessitate robust suspension systems capable of withstanding harsh terrain conditions while ensuring vehicle stability, manoeuvrability, and passenger comfort. The study focuses on evaluating the structural integrity, performance, and behaviour of the suspension system under diverse load conditions and terrain scenarios. This project focuses on conducting a comprehensive structural analysis of an off-road vehicle suspension system using advanced engineering software tools, specifically CATIA and ANSYS. CATIA is a powerful computer-aided design (CAD) software known for its capabilities in creating detailed 3D models, while ANSYS is a leading finite element analysis (FEA) software used for simulating structural behaviour and analyzing mechanical systems. Keywords: suspension system, CATIA v5, ANSYS, iterations, FEA.

An improved ant colony algorithm based on Q-Learning for route planning of autonomous vehicle

In view of the problems existing in the path planning algorithms of unmanned vehicles, such as low search efficiency, slow convergence speed and easy to fall into the local optimal. Based on the characteristics of route planning for unmanned vehicles, this paper introduces Q-Learning into the traditional ant colony algorithm to enhance the learning ability of the algorithm in dynamic environment, so as to improve the overall efficiency of route search. By mapping pheromones into Q values in Q-learning, rapid search in complex environments is realized, and a collection-free path satisfying constraints is quickly found. The results of case analysis show that compared with the traditional ant colony algorithm and the improved ant colony algorithm considering reward and punishment factors, the improved ant colony algorithm based on Q-Learning can effectively reduce the number of iterations, shorten the path optimization time and path length and other performance indicators, and has many advantages in jumping out of the local optimal, improving the global search ability and improving the convergence speed, and has good adaptability and robustness in complex environments. It ensures the safety and stability of unmanned vehicles in complex environments.

Fuzzy sliding mode control of automotive semi-active suspension based on optimization convergence law

Vehicle suspension smoothness and stability are important indicators for evaluating vehicles. This paper firstly establishes a 1/4 automobile semi-active suspension model, which is used as a reference model for comparison by optimizing the canopy model, and secondly designs the sliding mode controller, including the tracking model error design and the control system design, and then incorporates the optimized saturation function convergence law to control the convergence speed, and then combines it with the fuzzy control to improve the accuracy and robustness of the control system. The optimized semi-active suspension control system will enhance the vehicle’s vibration, smoothness, and stability. The final simulation test results show that the optimized sliding mode control suspension body vertical acceleration is reduced by 26.9%, suspension dynamic deflection by 36.7%, and tire dynamic load dynamic load is reduced by 28.4%.

An interconnected suspension with adjustable roll and pitch stiffness (IS-ARPS) to enhance anti-roll and anti-dive/squat characteristics

The suspension system is critical in maintaining vehicle stability in roll and pitch directions. Hydro-pneumatic interconnected suspension systems, in which the suspension struts are connected through hydraulic hoses, flow control valves, and accumulators, have great potential to further enhance anti-roll and anti-pitch characteristics. To address the existing designs’ shortcomings, this study introduces an interconnected suspension with adjustable roll and pitch stiffness (IS-ARPS). It not only eliminates the conventional anti-roll bars but also enables adjustment of roll stiffness depending on the driving and vehicle load conditions. In the pitch direction, the IS-ARPS provides an additional amount of pitch stiffness to reduce the dive/squat during braking/acceleration. The mathematical modeling of the IS-ARPS is also straightforward, requiring less computational cost and making it more practical for real-time implementations. Overall, the IS-ARPS system represents a significant advancement in the design of roll and pitch coupled interconnected suspensions, offering a more practical and versatile solution. The above features are examined through co-simulation between MATLAB/Simulink and ADAMS/Car.

Stability control of single-wheel failure in four-wheel independent drive electric vehicles

The integration of the motor and transmission mechanism into the wheels of a four-wheel independent drive electric vehicle with hub motors increases the frequency of failures, especially torque output failures, which seriously affect vehicle stability and jeopardize driving safety. This paper focuses on the perspective of single-wheel torque output failure and studies the vehicle stability control strategy. Firstly, based on the constructed simulation dynamics model, a slip controller is designed to obtain the direct lateral moment. Then, based on the control allocation principle, upper-level lateral moment allocation rules are designed, and torque allocation rules under single-wheel torque output failure are formulated. Finally, simulation experiments are conducted under different operating conditions. The simulation results show that this control strategy can effectively control vehicle stability under single-wheel failure conditions and ensure safe driving.

Advancements in Bicycle Safety: Integrating Control Sensors and Artificial Intelligence for Enhanced Airbag Innovation

Abstract: Motorcycle safety is a critical concern in traffic systems worldwide. This paper introduces a novel approach to enhancing rider safety through the integration of airless tires and advanced airbag systems, underpinned by artificial intelligence (AI) and control sensors. Airless tires contribute to vehicle stability by eliminating the risk of sudden deflation, while AI-driven airbag systems promise dynamic protection for riders during collisions. The study begins with an analysis of airless tire technology, emphasizing its impact on motorcycle stability and safety. It then transitions to the development of motorcyclespecific airbag systems, which utilize AI to process sensor data and make real-time decisions regarding airbag deployment. The effectiveness of these systems is validated through crash simulation tests and impact force measurements, demonstrating a substantial reduction in injury severity. Challenges such as cost, user acceptance, and technical constraints are thoroughly examined. The paper concludes with a discussion on future trends, including the potential for AI to predict and prevent accidents before they occur, thereby setting a new standard for motorcycle safety.

Coordinated control of yaw and roll stability in heavy vehicles considering dynamic safety requirements

In the field of heavy vehicle stability research, traditional safety requirements are often based on static scenario settings. However, the complexity and variability of actual road environments require safety control strategies that can be adapted to different driving conditions and environmental changes in real-time. To address this challenge, the paper proposes a coordinated control strategy for yaw and roll stability that considers the dynamic safety requirements. First, a quantitative analysis method for vehicle stability is proposed based on the dissipated energy theory, taking into account the lateral-vertical dynamics coupling characteristics. Additionally, a dynamic safety requirements identification model is developed by integrating the vehicle's future driving risk prediction algorithm. To meet dynamic safety requirements, a dynamic weight model predictive control method based on randomized ensembled double Q-learning reinforcement learning is designed. This method adjusts the control weights of yaw and roll stability online to flexibly address various destabilization risks, aiming to achieve more precise and efficient stability control. Through simulation and experimental verification, the results demonstrate that the proposed coordinated control strategy can effectively enhance the stability and safety of heavy vehicles in complex and dynamic driving environments.

Modeling and analysis of vehicle stability region based on Lyapunov and coordinated control

The timely intervention of the assisted driving system is the key to improving the handling stability and roll stability of the vehicle, and the vehicle stability region serves as the core basis for determining the intervention timing of the assisted driving system. With the aim of modeling and analyzing the vehicle stability region, a three-degree-of-freedom (3-DOF) vehicle dynamics model including yaw, roll, and lateral motions, as well as a nonlinear Magic Formula tire model are established in this paper. Based on this, a simplified but improved cubic tire model is developed to accurately fit the tire lateral force of Magic Formula tire model within a larger range of slip angles. Subsequently, using the Lyapunov method, the roll stability region and the yaw stability region are respectively constructed, and the accuracy verification and robustness analysis of the established stability region are conducted in the Matlab/Simulink environment. Finally, a model-free adaptive control method is employed to keep the vehicle state within the stability region, without tracking specific vehicle state objectives. The study in this paper can provide theoretical support for stability boundary determination, formulation of intervention timing for assisted driving stability control, and coordination control of vehicle stability and anti-rollover with local compatibility or even conflicts.

Model Predictive Control for Trajectory Planning Considering Constraints on Vertical Load Variation

To address the issue of centrifugal force affecting the vertical load during the stability and trajectory planning of autonomous vehicles during high-speed cornering and obstacle avoidance, a model predictive control of trajectory planning and tracking is proposed that considers the roll factor using only a two-degrees-of-freedom vehicle dynamics model. Firstly, a trajectory planning controller is designed. As a predictive model, a dual-track two-degrees-of-freedom vehicle dynamics model is established. This model describes the relationship between tire lateral forces and vertical loads using a quadratic nonlinear tire model. To reflect the actual dynamic state of the vehicle, the controller incorporates a nonlinear constraint that considers vertical load variations. The nonlinear optimization problem is transformed into a simplified quadratic programming problem by using the Jacobian matrix method to linearize the constraints. By fitting a fourth-degree polynomial curve to the discrete points calculated by the replanning algorithm, an optimal collision-free trajectory is obtained. Secondly, an MPC trajectory tracking controller is designed to control the vehicle in real time along the optimal trajectory from the planning, incorporating control quantity constraints, control increment constraints, and lateral angle constraints to maintain the vehicle’s motion state. We transform the trajectory tracking control problem into a quadratic programming problem, solving for the optimal control sequence for the autonomous vehicle to track the trajectory, achieving an optimized solution and rolling time domain control. Finally, the effectiveness of the vehicle’s obstacle avoidance planning and tracking under high-speed double-lane-change maneuver conditions is validated using the Simulink simulation platform. The results indicate that the designed planning and tracking controllers effectively improve the obstacle avoidance planning and tracking control for high-speed autonomous vehicles.