Vehicle Dynamics Stability Control Research Articles

A Three-Dimensional Integrated Non-Linear Coordinate Control Framework for Combined Yaw- and Roll-Stability Control during Tyre Blow-Out.

A tyre blow-out can greatly affect vehicle stability and cause serious accidents. In the literature, however, studies on comprehensive three-dimensional vehicle dynamics modelling and stability control strategies in the event of a sudden tyre blow-out are seriously lacking. In this study, a comprehensive 14 degrees-of-freedom (DOF) vehicle dynamics model is first proposed to describe the vehicle yaw-plane and roll-plane dynamics performance after a tyre blow-out. Then, based on the proposed 14 DOF dynamics model, an integrated control framework for a combined yaw plane and roll-plane stability control is presented. This integrated control framework consists of a vehicle state predictor, an upper-level control mode supervisor and a lower-level 14 DOF model predictive controller (MPC). The state predictor is designed to predict the vehicle’s future states, and the upper-level control mode supervisor can use these future states to determine a suitable control mode. After that, based on the selected control mode, the lower-level MPC can control the individual driving actuator to achieve the combined yaw plane and roll plane control. Finally, a series of simulation tests are conducted to verify the effectiveness of the proposed control strategy.

Robust LMI-Based H-Infinite Controller Integrating AFS and DYC of Autonomous Vehicles With Parametric Uncertainties

Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> -infinite feedback algorithm for vehicle dynamics stability control, and this algorithm controls vehicle steering system and brake system via direct yaw moment control (DYC) and active front steering control (AFS). The presented controller is robust against vehicle parametric uncertainties, including the vehicle mass and vehicle longitudinal velocity. A linear parameter varying lateral model is constructed utilizing polytopic uncertainty method considering time-varying vehicle longitudinal velocity and mass, where a polytope that contains finite vertices is established to contain all of the possible selections for uncertainty parameters. Then, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> -infinite feedback controller integrating DYC and AFS is derived via LMI technique. Finally, experimental results based on hardware-in-the-loop (HIL) platform illustrate that the presented controller has better performance of ensuring autonomous vehicle dynamics stability than other controllers.

A real-time approach to robust identification of tyre–road friction characteristics on mixed-μ roads

ABSTRACTThe interaction between the tyre and the road is crucial for understanding the dynamic behaviour of a vehicle. The road–tyre friction characteristics play a key role in the design of braking, traction and stability control systems. Thus, in order to have a good performance of vehicle dynamic stability control, real-time estimation of the tyre–road friction coefficient is required. This paper presents a new development of an on-line tyre–road friction parameters estimation methodology and its implementation using both LuGre and Burckhardt tyre–road friction models. The proposed method provides the capability to observe the tyre–road friction coefficient directly using measurable signals in real-time. In the first step of our approach, the recursive least squares is employed to identify the linear parameterisation form of the Burckhardt model. The identified parameters provide, through a T–S fuzzy system, the initial values for the LuGre model. Then, a new LuGre model-based nonlinear least squares parameter estimation algorithm using the proposed static form of the LuGre to obtain the parameters of LuGre model based on recursive nonlinear optimisation of the curve fitting errors is presented. The effectiveness and performance of the algorithm are demonstrated through the real-time model simulations with different longitudinal speeds and different kinds of tyres on various road surface conditions in both Matlab/Carsim environments as well as collected data from real experiments on a commercial trailer.

An integrated artificial neural network–unscented Kalman filter vehicle sideslip angle estimation based on inertial measurement unit measurements

Vehicle dynamics stability control systems rely on the amount of so-called sideslip angle and yaw rate. As the sideslip angle can be measured directly only with very expensive sensors, its estimation has been widely studied in the literature. Because of the large non-linearities and uncertainties in the dynamics, model-based methods are not a good solution to estimate the sideslip angle. On the contrary, machine learning techniques require large datasets that cover the entire working range for a correct estimation. In this paper, we propose an integrated artificial neural network and unscented Kalman filter observer using only inertial measurement unit measurements, which can work as a standalone sensor. The artificial neural network is trained solely with numerical data obtained with a Vi-Grade model and outputs a pseudo-sideslip angle which is used as input for the unscented Kalman filter. This is based on a kinematic model making the filter completely transparent to model uncertainty. A direct integration with integral damping and integral reset value allows the estimation of the longitudinal velocity of the kinematic model. A modification strategy of the pseudo-sideslip angle is then proposed to improve the convergence of the filter’s output. The algorithm is tested on both numerical data and experimental data. The results show the effectiveness of the solution.

A vehicle stability control strategy with adaptive neural network sliding mode theory based on system uncertainty approximation

ABSTRACTModelling uncertainty, parameter variation and unknown external disturbance are the major concerns in the development of an advanced controller for vehicle stability at the limits of handling. Sliding mode control (SMC) method has proved to be robust against parameter variation and unknown external disturbance with satisfactory tracking performance. But modelling uncertainty, such as errors caused in model simplification, is inevitable in model-based controller design, resulting in lowered control quality. The adaptive radial basis function network (ARBFN) can effectively improve the control performance against large system uncertainty by learning to approximate arbitrary nonlinear functions and ensure the global asymptotic stability of the closed-loop system. In this paper, a novel vehicle dynamics stability control strategy is proposed using the adaptive radial basis function network sliding mode control (ARBFN-SMC) to learn system uncertainty and eliminate its adverse effects. This strategy adopts a hierarchical control structure which consists of reference model layer, yaw moment control layer, braking torque allocation layer and executive layer. Co-simulation using MATLAB/Simulink and AMESim is conducted on a verified 15-DOF nonlinear vehicle system model with the integrated-electro-hydraulic brake system (I-EHB) actuator in a Sine With Dwell manoeuvre. The simulation results show that ARBFN-SMC scheme exhibits superior stability and tracking performance in different running conditions compared with SMC scheme.

Improvement in the vehicle stability of distributed-drive electric vehicles based on integrated model-matching control

This paper presents a vehicle dynamic stability controller for distributed-drive electric vehicles. A hierarchical control structure is adopted for the proposed controller. An upper controller is designed on the basis of integrated model-matching control. It consists of a feedforward component plus a feedback component to calculate the desired external yaw moment to achieve the desired vehicle motion. The feedforward control aims at compensating the effect caused by the variation in the linear cornering stiffnesses of the tyres during the life cycle of the tyres. It provides a rapid response under common driving conditions. The linear cornering stiffnesses of the tyres are estimated in real time by the adaptive forgetting-factor recursive least-squares method. Since many vehicle parameters have strongly non-linear and time-varying characteristics, adaptive sliding mode control is used as the feedback component to make the controller robust against systematic uncertainties. To combine the outputs of feedforward and feedback together and to avoid probable conflict, a weight gain coefficient is obtained. Additionally, a conventional sliding-mode controller is introduced as a comparative upper control strategy. The lower controller is utilized to allocate the required yaw moment and traction to the four independent motors, taking into account the tyre grip margins. Simulations for a low- g manoeuvre and a high- g manoeuvre are carried out to evaluate the proposed control algorithm. The results show that the proposed vehicle stability controller can significantly stabilize the vehicle motion and greatly reduce the driver’s workload in comparison with with the conventional sliding-mode controller.

Multi-axle vehicle dynamics stability control algorithm with all independent drive wheel

The stability driving characteristic and the tire wear of 8-axle vehicle with 16-independent driving wheels are discussed in this paper. The lateral stability of 8-axle vehicle can be improved by the direct yaw moment which is generated by the 16 independent driving wheels. The hierarchical controller is designed to determine the required yaw torque and driving force of each wheel. The upper level controller uses feed-forward and feed-backward control theory to obtain the required yaw torque. The fuzzification weight ratio of two control objective is built in the upper level controller to regulate the vehicle yaw and lateral motions. The rule-based yaw moment distribution strategy and the driving force adjustment based on the safety of vehicle are proposed in the lower level controller. The influence of rear steering angle is considered in the distribution of driving force of the wheel. Simulation results of a vehicle double lane change show the stability of 8-axle vehicle under the proposed control algorithm. The wear rate of tire is calculated by the interaction force between the tire and ground. The wear of tire is different from each other for the vehicle with the stability controller or not.

Calibration of vehicle dynamics and stability control model

This publication describes a process to develop and calibrate a physics-based model for vehicle stability control. The approach consists of two phases. In the first phase, a baseline vehicle dynamics model was established using the commercially available simulation tool CarSim. The model was calibrated and validated for a variety of steady state, transient state and brake intervention handling manoeuvres using experimental test data. In the second phase, a simplified stability control model was developed in Matlab/Simulink based on representative control strategies. Subsequently, the vehicle dynamics model and stability control model developed were coupled using co-simulation and calibrated such that the closed-loop system behaviour approximated the real world test behaviour as best as possible. Finally, two case studies were developed to showcase the application of the closed-loop mathematical model in simulation-based studies addressing aftermarket vehicle modifications and their implication on vehicle stability control behaviour.

Calibration of vehicle dynamics and stability control model

This publication describes a process to develop and calibrate a physics-based model for vehicle stability control. The approach consists of two phases. In the first phase, a baseline vehicle dynamics model was established using the commercially available simulation tool CarSim. The model was calibrated and validated for a variety of steady state, transient state and brake intervention handling manoeuvres using experimental test data. In the second phase, a simplified stability control model was developed in Matlab/Simulink based on representative control strategies. Subsequently, the vehicle dynamics model and stability control model developed were coupled using co-simulation and calibrated such that the closed-loop system behaviour approximated the real world test behaviour as best as possible. Finally, two case studies were developed to showcase the application of the closed-loop mathematical model in simulation-based studies addressing aftermarket vehicle modifications and their implication on vehicle stability control behaviour.

Genetic algorithm–based varying parameter linear quadratic regulator control for four-wheel independent steering vehicle

From the perspective of vehicle dynamics, the four-wheel independent steering vehicle dynamics stability control method is studied, and a four-wheel independent steering varying parameter linear quadratic regulator control system is proposed with the help of expert control method. In the article, a four-wheel independent steering linear quadratic regulator controller for model following purpose is designed first. Then, by analyzing the four-wheel independent steering vehicle dynamic characteristics and the influence of linear quadratic regulator control parameters on control performance, a linear quadratic regulator control parameter adjustment strategy based on vehicle steering state is proposed to achieve the adaptive adjustment of linear quadratic regulator control parameters. In addition, to further improve the control performance, the proposed varying parameter linear quadratic regulator control system is optimized by genetic algorithm. Finally, simulation studies have been conducted by applying the proposed control system to the 8-degree-of-freedom four-wheel independent steering vehicle dynamics model. The simulation results indicate that the proposed control system has better performance and robustness and can effectively improve the stability and steering safety of the four-wheel independent steering vehicle.

A novel vehicle dynamics stability control algorithm based on the hierarchical strategy with constrain of nonlinear tyre forces

Direct yaw moment control (DYC), which differentially brakes the wheels to produce a yaw moment for the vehicle stability in a steering process, is an important part of electric stability control system. In this field, most control methods utilise the active brake pressure with a feedback controller to adjust the braked wheel. However, the method might lead to a control delay or overshoot because of the lack of a quantitative project relationship between target values from the upper stability controller to the lower pressure controller. Meanwhile, the stability controller usually ignores the implementing ability of the tyre forces, which might be restrained by the combined-slip dynamics of the tyre. Therefore, a novel control algorithm of DYC based on the hierarchical control strategy is brought forward in this paper. As for the upper controller, a correctional linear quadratic regulator, which not only contains feedback control but also contains feed forward control, is introduced to deduce the object of the stability yaw moment in order to guarantee the yaw rate and side-slip angle stability. As for the medium and lower controller, the quantitative relationship between the vehicle stability object and the target tyre forces of controlled wheels is proposed to achieve smooth control performance based on a combined-slip tyre model. The simulations with the hardware-in-the-loop platform validate that the proposed algorithm can improve the stability of the vehicle effectively.

Real-time yaw rate prediction based on a non-linear model and feedback compensation for vehicle dynamics control

The yaw rate is a key state for vehicle dynamics stability control. However, the time delay from the steering input to the yaw rate measured by the yaw rate sensor changes with the frequency of the steering input and can also be affected by the road friction and the vehicle speed. In order to reduce the time delay effect on the vehicle dynamics stability controller, a new comprehensive yaw rate prediction method is proposed. A seven-degree-of-freedom non-linear vehicle model is adopted as the prediction model. A quadratic polynomial extrapolation method is used to compute the future steering-wheel input in the prediction horizon. A model-based yaw rate prediction algorithm is combined with a feedback compensator to guarantee the robustness of the algorithm. Meanwhile, a linear extrapolation method is utilized to minimize the defects of model-based prediction since the simplified vehicle model cannot fully model the hysteresis or other non-linearities of the vehicle systems. Experimental tests on the vehicle show that the combined algorithm is able to predict the yaw rate about 100 ms earlier than the sensor measurement is. Simulations show that the vehicle stability control strategy based on the proposed comprehensive prediction method has a better performance than the traditional control strategy does.

Progress on Vehicle Dynamics Stability Control System

vehicle dynamics stability control system(DSC) is a key technology of vehicle active safety system, and it has been a hot research issue in vehicle field for a long time. The DSC system which is integrated with anti-lock braking system(ABS), traction control system(TCS) and active yaw control(AYC) can improve the stability and safety of vehicle significantly. The research progress of vehicle dynamics stability control system can be classified into modeling, state observation, control strategy and industrialization. The modeling includes the modeling in simulation and the modeling in control. ADAMS or Carsim can be adopted to build the vehicle model for simulation. Two wheels model or four wheels model usually are used in control. The state observation mainly focuses on the pressure estimation of the wheel cylinder, the estimation of road friction, the estimation of tire forces, the estimation of longitudinal and lateral velocity. After the realization of the basic function of vehicle stability control, the requirement for control is increasing gradually. A new AYC control strategy which is based on the predicted yaw rate is introduced to reduce the hysteresis of control. Meanwhile a new kind of TCS control strategy is introduced to reduce the vehicle vibration on uneven low-adhesion road. Through the unremitting exploration and research, the industrialization of DSC is gradual realizing.

Vehicle dynamic stability improvements through gain-scheduled steering and braking control

This paper is concerned with the synthesis of a robust gain-scheduled ℋ∞ MIMO vehicle dynamic stability controller (VDSC) involving both steering and rear braking actuators. This VDSC aims at improving automotive vehicle yaw stability and lateral performances. The aim of this work is to provide a methodology to synthesise such a controller while taking into account the braking actuator limitations and use the steering actuator only if it is necessary. These objectives are treated in an original way by the synthesis of a parameter-dependent controller built in the LPV framework and by the solution of an LMI problem. The proposed solution is coupled with a local ABS strategy to guarantee slip stability and make the solution complete. Nonlinear time and frequency domain simulations on a complex full vehicle model (which has been validated on a real car), subject to critical driving situations, show the efficiency and robustness of the proposed solution.

Real-time Tire Parameters Observer for Vehicle Dynamics Stability Control

The performance of the vehicle dynamics stability control system(DSC) is dominated by the accurate estimation of tire forces in real-time.The characteristics of tire forces are determined by tire dynamic states and parameters,which vary in an obviously large scope along with different working conditions.Currently,there have been many methods based on the nonlinear observer to estimate the tire force and dynamic parameters,but they were only used in off-line analysis because of the computation complexity and the dynamics differences of four tires in the steering maneuver conditions were not considered properly.This paper develops a novel algorithm to observe tire parameters in real-time controller for DSC.The algorithm is based on the sensor-fusion technology with the signals of DSC sensors,and the tire parameters are estimated during a set of maneuver courses.The calibrated tire parameters in the control cycle are treated as the elementary states for vehicle dynamics observation,in which the errors between the calculated and the measured vehicle dynamics are used as the correcting factors for the tire parameter observing process.The test process with a given acceleration following a straight line is used to validate the estimation method of the longitudinal stiffness;while the test process with a given steering angle is used to validate the estimated value of the cornering stiffness.The ground test result shows that the proposed algorithm can estimate the tire stiffness accurately with an acceptable computation cost for real-time controller only using DSC sensor signal.The proposed algorithm can be an efficient algorithm for estimating the tire dynamic parameters in vehicle dynamics stability control system,and can be used to improve the robustness of the DSC controller.

Road Friction Estimation under Complicated Maneuver Conditions for Active Yaw Control

Road friction coefficient is a key factor for the stability control of the vehicle dynamics in the critical conditions. Obviously the vehicle dynamics stability control systems,including the anti-lock brake system(ABS),the traction control system(TCS),and the active yaw control(AYC) system,need the accurate tire and road friction information. However,the simplified method based on the linear tire and vehicle model could not obtain the accurate road friction coefficient for the complicated maneuver of the vehicle. Because the active braking control mode of AYC is different from that of ABS,the road friction coefficient cannot be estimated only with the dynamics states of the tire. With the related dynamics states measured by the sensors of AYC,a comprehensive strategy of the road friction estimation for the active yaw control is brought forward with the sensor fusion technique. Firstly,the variations of the dynamics characteristics of vehicle and tire,and the stability control mode in the steering process are considered,and then the proper road friction estimation methods are brought forward according to the vehicle maneuver process. In the steering maneuver without braking,the comprehensive road friction from the four wheels may be estimated based on the multi-sensor signal fusion method. The estimated values of the road friction reflect the road friction characteristic. When the active brake involved,the road friction coefficient of the braked wheel may be estimated based on the brake pressure and tire forces,the estimated values reflect the road friction between the braked wheel and the road. So the optimal control of the wheel slip rate may be obtained according to the road friction coefficient. The methods proposed in the paper are integrated into the real time controller of AYC,which is matched onto the test vehicle. The ground tests validate the accuracy of the proposed method under the complicated maneuver conditions.

Control Strategy of Vehicle Dynamic Stability Control System on Low Tire-road Friction Condition

The tire-road friction characters and the tire force characters on the low friction conditions are studied.The 2-DOF vehicle model with four wheels is developed based on the HSRI tire model,and then the vehicle dynamics stability characteristic is studied for the real driving conditions.Applying the fl method,the controllable range of the vehicle dynamics stability control system is analyzed.Then the characters of the vehicle stability,the axle stability,and the tire stability are integrated to study the control objects of the differences of the vehicle slip angle and the yaw rate.Based on the analysis,the vehicle dynamics stability control strategy on the low friction condition is brought forward,and then verified by the hardware-in-the-loop-system simulation and the real vehicle test on the winter test ground.

Fast estimation and compensation of the tyre force in real time control for vehicle dynamic stability control system

This paper develops a novel algorithm to estimate and compensate the tyre force for the vehicle Dynamic Stability Control (DSC). The algorithm is based on the active brake control of the full subsystems, and the robustness is achieved by using the compensation method. With this algorithm, the vehicle DSC system will precisely prevent the vehicle deviating from the normal attitude and improve the system's stability in the limited handling conditions. The results of the software-in-the-loops simulation and the ground test in the real car show the effectiveness of the proposed algorithm when the vehicle is subjected to limited handling manoeuvres even in low friction condition.