Vehicle Sideslip Research Articles

Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque

The sideslip angle is crucial for the lateral stability state and stability control of intelligent commercial vehicles. However, sensors that can be used for direct measurements are often complex, expensive, and difficult to install in commercial vehicles. To estimate the vehicle sideslip angle, a state observer derived from the extended Kalman filter (EKF) method is proposed, and the state observer is estimated based on steering torque rather than steering angle. The transfer functions between the sideslip angle–steering torque and sideslip angle–steering angle are established, respectively, and the analysis shows that the steering torque signal has a more rapid and more direct reaction due to the hydraulic pressure in the steering system. Finally, the proposed method is validated using Simulink/TruckSim simulation hardware-in-the-loop bench test, and the results show that the proposed method can accurately reflect the actual state of the sideslip angle with good reliability and effectiveness.

Robust Virtual Sensing of the Vehicle Sideslip Angle through the Cross-Combination of Multiple Filters Using a Decision Tree Algorithm.

This paper presents a state-of-the-art estimation technique by cross-combining a number n of filters for high-precision, reliable and robust vehicle sideslip angle state estimation, over a full range of vehicle operations irrespective of the driving mission and disruptions that may occur in the system. A machine-learning algorithm based on decision trees connects several filters together to switch between them according to the driving context, ensuring the best possible state estimate for relatively small and large sideslip angle values. In conjunction with the above-mentioned aspects, a seamless transition between different vehicle models is attained by observing the key parameters characterizing the lateral motion of the vehicle. The tests conducted using a prototype vehicle on a snow-covered track confirm the effectiveness and reliability of the proposed approach.

Coordination Control of Active Steering and Direct Yaw Control for the Articulated Steering Vehicle

Lateral stability is vital to vehicle handling stability and traffic safety. It is also a crucial factor for the path-tracking ability of the vehicle in the intelligent transportation system (ITS). Most of the research focuses on vehicles with an Ackerman steering system. The ASV (articulated steering vehicle) has a lower steering radius. Thus, it is widely used in some special vehicles, such as mining and construction vehicles. The ASV has weaker lateral stability than the vehicle with an Ackerman steering system. To improve the stability of an ASV, the nonlinear lateral dynamic model is established and validated by field test. With the lateral dynamic model, the steering characteristic of the ASV is analyzed. Based on the stability criteria analysis, the vehicle sideslip angle and angular velocity phase portrait are chosen as the stability indicator. An integrated AASS (active articulated steering system) DYC (direct yaw control) controller based on the adaptive MPC (model predictive control) method is designed according to the track on the phase plane. The double lane-change and 0.7 Hz sine with dwell maneuver are initiated based on the integrated vehicle dynamic model. The results suggest that the provided controller has a better stability performance than the current antiwindup PID control algorithm. It lays a good foundation for the vehicle safety and path tracking of ASV in the ITS.

Robust H∞ Output Feedback Trajectory Tracking Control for Steer-by-Wire Four-Wheel Independent Actuated Electric Vehicles

This paper investigates the trajectory tracking control issue of four-wheel independently actuated electric vehicles (FWIA EVs) with steer-by-wire devices concerning parameter uncertainties, external disturbances, input saturation, and vehicle sideslip angle not easily obtained. A robust H∞ dynamic output feedback control strategy is proposed for the integrated control of the steering motor current and direct yaw moment without using sideslip angle information to ensure the reference trajectory tracking and the improvement of handling performance and yaw stability. In the proposed integration control framework, the steer-by-wire device dynamic is involved in the polyhedral linear parameter-varying (LPV) trajectory tracking error model considering the time-varying longitudinal velocity, and the norm-bounded parameter uncertainties such as road adhesion coefficient, tire cornering stiffness, vehicle mass, and vehicle moment of inertia. With the help of the LPV model of all the states of the steer-by-wire FWIA EV, a dynamic output feedback trajectory tracking controller is designed using the robust H∞ technique. The controller gain matrices are obtained by solving the linear matrix inequalities. Finally, the high-fidelity full-vehicle model based on the CarSim-MATLAB/Simulink joint simulation platform verifies the robustness and advantages of the designed control strategy in the accelerated lane-change scenario.

An XGBoost approach to detect driver visual distraction based on vehicle dynamics

Objectives Distracted driving such as reading phone messages during driving is risky, as it increases the probability of severe crashes. This study proposes an XGBoost model for visual distraction detection based on vehicle dynamics data from a driving simulation study. Methods A simulated driving experiment involving thirty-six drivers was launched. We obtained the vehicle dynamics parameters required for the model using the time window and fast Fourier transform methods, totaling 26 items. Meanwhile, the effects of varied time window sizes (1–7 s) and amount of input indications on model performance were studied. Results By conducting a comparative analysis, it has been determined that the ideal time window size is 5 s. Additionally, the optimal number of input indicators was found to be 23. The XGBoost model for distinguishing distractions achieved an accuracy rate of 85.68%, a precision rate of 85.83%, a recall rate of 83.85%, an F1 score of 84.82%, and an AUC value of 0.9319, which were higher than SVM and RF. The gain-based feature rank demonstrated that the standard deviation of vehicle sideslip rate and the mean amplitude of the 0–1 Hz spectrum component of the steering wheel angle were more crucial than other features. Conclusions The research results indicate that the steering wheel angle and vehicle sideslip angle may be more conducive to identifying distractions. This XGBoost model could potentially be applied in advanced driving assistant systems (ADAS) to warn driver and reduce cellphone involved distracted driving.

A novel hyperbolic tangent sliding mode observation of vehicle lateral force fed back by longitudinal force error

AbstractThe tyre lateral force control is crucial to vehicle lateral stability. Vehicle side slip and out of control can be prevented effectively by observing accurately the lateral force. Thus, a novel quasi‐sliding mode observer (QSMO) is proposed. The algorithm adopts the longitudinal tyre force error as feedback considering vehicle parameter uncertainties and without a complex tyre model. First, the on‐line verification of the algorithm was carried out by dSPACE for using the experimental data of the real vehicle linear acceleration and deceleration conditions, and comparison of experimental output with different observation algorithms. Further, the simulation under emergency obstacle avoidance conditions and the double‐line shifting conditions were conducted to verify the accuracy of the algorithm respectively. Simulation results show that the percentage errors between the tyre lateral forces from the proposed QSMO and the actual data are less than 5.35%, and the prediction accuracy of the QSMO by 38.78% is higher than that of the conventional first‐order SMO (FSMO), which indicates that the QSMO is superior to the FSMO.

Stability control of distributed drive electric vehicle based on adaptive fuzzy sliding mode

To improve the driving stability of a distributed drive electric vehicle (DDEV), we propose a hierarchical direct yaw moment control strategy. In the upper-level controller, an adaptive fuzzy sliding mode controller (AFSMC) is designed with joint control objectives of sideslip angle and yaw rate. In the conventional controller, the weight coefficients of the joint control objectives are fixed values, which reduces the adaptability of the system. To improve this problem and achieve adaptive adjustment of the weight coefficients of the control objectives with the vehicle motion state, an adaptive fuzzy control-based target weight coefficient controller is designed. In the lower controller, a quadratic programing method is used to assign torque with the optimization objective of minimizing tire attachment utilization. A joint simulation with Carsim and Simulink software was performed to compare and analyze the AFSMC vehicle and the traditional SMC vehicle under the steering wheel turning angle sinusoidal input and double shift line conditions. The results show that AFSMC effectively reduces the magnitude of vehicle sideslip angle, yaw rate, and lateral acceleration, enhances the driving stability of the vehicle, and enables adaptive adjustment of the weight coefficients of the joint control objectives, improving the adaptability of the controller. Finally, the effectiveness of the control strategy is verified by hardware-in-the-loop (HIL) test.

Online Learning-Informed Feedforward-Feedback Controller Synthesis for Path Tracking of Autonomous Vehicles

High-performance path tracking is a key technology for autonomous vehicles. Feedforward-feedback control architectures are suitable for accurate path tracking with adequate margins of stability. For system modelling in the feedforward component, the learning-based method has been proven to be a promising approach owing to its model-free framework. However, the offline-learned data model trained with collection data is confined by its feature space, resulting in insufficient generalization. As a solution, in this study, we introduce an online learning network – the recurrent high-order neural network (RHONN) – to characterize vehicle behaviors. The RHONN is used to feature vehicle behaviors in a timely manner with a high fidelity and flexible form. The equilibrium at the preview point on the desired path is found based on the online-identified RHONN model, and its induced steering angle is taken as the feedforward command. For the feedback steering controller, the preview point position-based control law incorporating the steady vehicle sideslip angle is adopted to enhance the stability performance. Finally, in the CarSim/Simulink environment, the performance of the designed RHONN-informed feedforward and feedback controller is validated in two typical scenarios – double-lane change and single-turn. The validation results reveal that the proposed approach offers better tracking accuracy in linear and nonlinear regions than other techniques. More notably, the average execution time (3.55 ms) is less than the sampling frequency of the controller (50 ms), which further confirms the applicability and efficiency of the proposed approach.

Research on control strategy of vehicle stability based on dynamic stable region regression analysis.

The intervention time of stability control system is determined by stability judgment, which is the basis of vehicle stability control. According to the different working conditions of the vehicle, we construct the phase plane of the vehicle's sideslip angle and sideslip angular velocity, and establish the sample dataset of the stable region of the different phase planes. To reduce the complexity of phase plane stable region division and avoid large amount of data, we established the support vector regression (SVR) model, and realized the automatic regression of dynamic stable region. The testing of the test set shows that the model established in this paper has strong generalization ability. We designed a direct yaw-moment control (DYC) stability controller based on linear time-varying model predictive control (LTV-MPC). The influence of key factors such as centroid position and road adhesion coefficient on the stable region is analyzed through phase diagram. The effectiveness of the stability judgment and control algorithm is verified by simulation tests.

RHONN-Modeling-Based Predictive Safety Assessment and Torque Vectoring for Holistic Stabilization of Electrified Vehicles

Improving vehicle safety and reducing traffic accidents have always been of cardinal importance in vehicle dynamics control fields. A reasonable and comprehensive safety index that characterizes the vehicle's safe region is the most challenging aspect of research. With the linear dynamics model as the benchmark, this article uses the deviation of yaw rate and vehicle sideslip angle from the corresponding linear response as the lateral stability indices. Meanwhile, the maximum slip ratio of the driven wheels is selected as the longitudinal stability index. The <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">safety indicator</i> , a quantitative index featuring the safety degree of vehicle planar motions, is then inferred by a fuzzy inference system using the lateral and longitudinal stability indices. As such, the recurrent high-order neural network model predicts the vehicle states. Based on the predicted states, a safety indicator is then derived by using fuzzy inference system, which can assess the safety of a driver's control commands. In the case of an improper driving torque demand given by the driver, the torque correction process is immediately conducted to maintain the vehicle in a safe region. Finally, two typical scenarios—slippery curves and double lane changes in low friction roads—are simulated on the MATLAB/Simulink-CarSim cosimulation platform. The hardware-in-the-loop experiments are also conducted on a driving simulator test rig, validating the performance of the developed algorithms. The holistic stability performance of the in-wheel motor driven vehicle is thoroughly analyzed and compared using three existing methods. The simulation and experimental results validate the effectiveness and feasibility of the proposed method.

Autonomous Vehicle Kinematics and Dynamics Synthesis for Sideslip Angle Estimation Based on Consensus Kalman Filter

An autonomous vehicle sideslip angle estimation algorithm is proposed based on consensus and vehicle kinematics/ dynamics synthesis. Based on the velocity error measurements between the reduced Inertial Navigation System (R-INS) and the global navigation satellite system (GNSS), a velocity-based Kalman filter is formalized to estimate the velocity errors, attitude errors, and gyro bias errors of the R-INS. The observability issue of the heading error, which affects sideslip estimation, is analyzed. Then, to enhance the observability and improve the estimation accuracy of the heading error under normal driving conditions, a consensus Kalman information filter is developed to synthesize the vehicle kinematics and dynamics and estimate the heading error. Within the developed consensus framework, one node augments a novel heading error measurement from a linear vehicle-dynamic-based sideslip estimator and another node adopts the heading error from the GNSS course. Next, based on the vehicle lateral excitation level, a weighting scheme is proposed to fuse the error state estimates from the velocity-based and consensus Kalman state observers. The stability of the proposed state observers is also investigated. Comprehensive experimental studies, including critical slalom, slight/normal double lane change, and normal driving maneuvers, were conducted to verify the proposed estimation framework; they confirm the reliability and accuracy of the estimator in various automated driving conditions even in comparison with state-of-the-art methods that utilize more measurements (dual-antenna GNSS). Also, this novel multisensor framework is extendable to leverage speed information from other sensors such as cameras and light detection and ranging (LiDAR) to increase reliability and accuracy.

Estimation method of vehicle sideslip angle considering coupled longitudinal and lateral dynamics

Estimation method of vehicle sideslip angle considering coupled longitudinal and lateral dynamics

Estimation method of vehicle sideslip angle considering coupled longitudinal and lateral dynamics

Estimation method of vehicle sideslip angle considering coupled longitudinal and lateral dynamics

Robust observer design for LPV systems using Kronecker sum and direct searching

This paper addresses a robust Luenberger observer for an uncertain linear parameter varying (LPV) system based on the concepts of direct searching, Kronecker sum, and small gain theorem. An algorithm is proposed to investigate the situation of the different parts of the initial observer design space, in terms of suitability for being selected as the observer gain or needing elimination due to being an undesirable part. To find the undesirable parts of the design space, the singularity of the Kronecker sum matrix of the estimation error is checked by employing the small gain theorem concept. By excluding undesired parts from the entire design space and checking the remaining parts’ feasibility, a suitable solution can be found, appropriately. The performance of the proposed observer design method is illustrated by using numerical examples and an electric ground vehicle (EGV) system for the estimation of the vehicle sideslip angle and the yaw rate. The results prove the appropriateness of the designed observer using the proposed approach.

Research on Direct Yaw Moment Control of Electric Vehicles Based on Electrohydraulic Joint Action

To solve the problem of lateral instability of the vehicle caused by insufficient lateral force of the tires due to the insufficient torque provided by the motor to the tire when the vehicle turns sharply or avoids obstacles in an emergency, a layered control method is used to design a lateral stability control system. The upper decision layer selects the yaw rate and the sideslip angle of the center of mass as the control variables and uses the joint state deviation of the yaw rate and the sideslip angle of the center of mass and the rate of change of the deviation as the input of the sliding mode variable structure controller to calculate the additional yaw moment required to maintain vehicle stability. The lower torque distribution layer realizes the distribution of torque through the electro-hydraulic coordinated control method: the torque distribution rule based on real-time load transfer calculates the torque corresponding to the control wheel and generates the torque through the hub motor and transmits it to the wheel. When the torque output from the motor cannot provide sufficient torque for the vehicle, hydraulic braking is used as a compensating control, and the difference between the required yaw torque and the motor-generated yaw torque is used as the required torque for hydraulic control to calculate the wheel cylinder pressure required to brake the wheels. Based on the joint simulation model of MATLAB/Simulink and Carsim, the sine and double shift line working condition are selected for stability simulation experiments. From the simulation results, it can be seen that the yaw rate and sideslip angle of the center of mass of the vehicle with sliding mode control and electro-hydraulic coordinated control almost coincide with the ideal value curve, which are both smaller than the output parameters of the uncontrolled vehicle. From the perspective of the motor output torque, compared with pure motor control, the effect of electro-hydraulic coordinated control is better, and the hydraulic system can compensate for the braking torque in time and enhance the lateral stability of the vehicle. The designed control strategy can make the yaw rate and the sideslip angle of the center of mass of the vehicle follow the reference value better, which can effectively avoid the vehicle sideslip and instability and improve the vehicle yaw stability and driving safety. However, due to the limitations of experimental equipment, the proposed method could not be applied to the real vehicle test. The real vehicle test can better test the control effect of the proposed method.

Event-Triggered Vehicle Sideslip Angle Estimation Based on Low-Cost Sensors

Accurate vehicle sideslip angle estimation is crucial for vehicle stability control. In this article, an enabling event-triggered sideslip angle estimator is proposed by using the kinematic information from a low-cost global positioning system (GPS) and an on-board inertial measurement unit (IMU). First, a preliminary vehicle sideslip angle is derived using the heading angle of GPS and the yaw rate of IMU, and an event-triggered mechanism is proposed to eliminate the accumulative estimation error. The algorithm convergence is guaranteed through theoretical deduction. Second, a longitudinal and a lateral vehicle velocity are obtained using the preliminary vehicle sideslip angle and the measured GPS velocity and their kinematic relationship, based on which a multisensor fusion and a multistep Kalman filter scheme are, respectively, presented to realize longitudinal and lateral vehicle velocity estimation. By doing this, the update frequency and estimation accuracy of the vehicle sideslip angle estimate can be further improved to meet the requirement of online implementation. Finally, the effectiveness and reliability of the proposed scheme are verified under comprehensive driving conditions through both hardware-in-loop (HIL) and field tests. The results show that the proposed event-triggered sideslip angle estimator has a mean estimation error of 0.029 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> and of 0.14 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> in the HIL and field tests, exhibiting better estimation accuracy, reliability, and real-time performance compared with other typical estimators.

Yaw stability control for steer-by-wire vehicle based on radial basis network and terminal sliding mode theory

To further improve the handling and steering performance of steer-by-wire vehicles, a layered control strategy is proposed for the steer-by-wire system, which includes the upper and lower controllers. In the upper control, considering the vehicle sideslip angle and yaw rate, an adaptive sliding mode control strategy is designed for the vehicle lateral stability. Due to the measurement difficulty of the sideslip angle, a robust sliding mode observer is designed to estimate the sideslip angle. In the lower control, a new adaptive global fast terminal sliding mode is proposed to ensure the tracking accuracy of the front wheel angle, considering the dynamic uncertainty characteristics of the steering-by-wire system. The adaptive part of the lower control adopts a radial basis function network, and the adaptive law is designed in Lyapunov sense to make the network approximate the unknown dynamics of the steering-by-wire system, thus the parameters of the steering-by-wire are not required to be completely known. Finally, the joint simulation of MATLAB/Simulink and Carsim verifies that the proposed layered controller has excellent control performance.

Vehicle Sideslip Angle Estimation Based on Interacting Multiple Model Kalman Filter Using Low-Cost Sensor Fusion

This study proposes a new method for vehicle sideslip angle estimation utilizing the competitively priced sensor fusion using in-vehicle sensors and low-cost standalone global positioning system (GPS). To estimate unmeasurable vehicle states, vehicle sideslip angle and tire cornering stiffness, an interacting multiple model (IMM) Kalman filter is proposed that combines two extended Kalman filters (EKFs), each including kinematic and dynamic equations of vehicle lateral velocity. To properly combine the outputs of these model-based EKFs, a weighted probability of each model based on the stochastic process is designed, which reflects the characteristics of each of the kinematic and dynamic equations in real-time. Also, the observability of the proposed estimation algorithm is checked by observability functions of nonlinear systems. The estimation performance in various driving scenarios is verified using an experimental vehicle, and its superiority is confirmed through a comparative study. The proposed algorithm makes the following main contributions for estimating the vehicle sideslip angle: 1) the high optimality of estimation results and 2) the accurate estimation of tire cornering stiffness.

Estimation of Angles of Attack and Sideslip of Unmanned Aerial Vehicle in the Absence of Aerodynamic Angle Sensors

A method for estimating aerodynamic angles in the absence of appropriate sensors is proposed, using measurements of three projections of flight speed carried out by the navigation system and the values of the orientation angles. The relevance of the problem being solved is determined by the fact that on unmanned aerial vehicles (UAVs) sensors of aerodynamic angles, that is, angles of attack and slip, are often not installed due to restrictions on dimensions and mass. The proposed method is based on the joint use of mathematical models of aircraft motion, known from flight dynamics, and the theory of parametric identification of dynamic systems. The key factor ensuring the accuracy of the proposed method is the use of very accurate measurements of three UAV velocity projections performed by a satellite navigation system or an inertial navigation system with satellite correction. To account for the influence of wind, parametric identification of three projections of wind speed is provided. Another feature of the method is that instead of the missing aerodynamic angle sensors, it is proposed to use information about the aerodynamic coefficients of the lifting and lateral forces of the UAV. If these coefficients are known with errors, their values are also specified by identification methods. The dimension of the identification problem turns out to be low in the range of small and medium angles of attack when the aerodynamic dependencies are linear. The results of testing the proposed method based on simulation data on the flight test bench of a modern training aircraft for nine different flight modes under conditions of simulating random errors of onboard measurements corresponding to the flight experiment are presented.

Research on a Real-Time Estimation Method of Vehicle Sideslip Angle Based on EKF.

In this article, a real-time vehicle sideslip angle state observer is proposed, which is based on the EKF algorithm. Firstly, based on a 2-DOF dynamical model and the tire lateral force model, the vehicle sideslip angle state observer model with a self-adapted truncation procedure is established by combining the EKF and the least squares methods. The calculation of the Jacobi matrix in the time domain is transformed into a calculation in the frequency domain. A self-adapted update noise estimation method and an initial value setting strategy are proposed as well. Finally, a hardware-in-the-loop simulation is carried out by Matlab/Simulink-CarSim-dSPACE, and the real-time reliability of the estimation method is verified and analyzed by RMSE.