Sideslip Angle Research Articles

Predictive handling limits monitoring and agility improvement with torque vectoring on a rear in-wheel drive electric vehicle

This work presents a predictive torque vectoring controller that optimally monitors the vehicle limits of handling using active torque distribution in the rear axle of a fully electric vehicle. It works in combination with a feedforward controller designed to improve the vehicle's agility. The overall torque vectoring strategy is described together with the vehicle lateral dynamics, sideslip angle estimator, and torque allocation method. Numerical simulations for various scenarios and road profiles show the benefits of predicting the vehicle's handling limits and the enhancement of vehicle stability in terms of reduced vehicle sideslip angle and driver effort. The proposed optimal control method for predicting vehicle handling limit violations does not require a dedicated solver, making it a promising candidate for real-time applications. The case study is a vehicle equipped with two rear in-wheel motors in the framework of HiPERFORM, an ECSEL Joint Undertaking (JU) European research project. Hardware-in-the-loop (HiL) tests were performed on a dedicated e-axle test bench to integrate the torque vectoring controller with the real e-motors and a dual inverter. The results of the HiL testing demonstrate that the torque-vectoring requirements are satisfied by the hardware configuration in use.

Estimation and Compensation of Heading Misalignment Angle for Train SINS/GNSS Integrated Navigation System Based on Observability Analysis

The inertial Navigation Systems/global navigation satellite system (SINS/GNSS) has become a research hotspot in the field of train positioning. However, during a uniform straight-line motion period, the heading misalignment angle of the SINS/GNSS is unobservable, resulting in the divergence of the heading misalignment angle and ultimately causing a divergence in the train’s speed and position estimation. To address this issue, this paper proposes an estimation and compensation method for the heading misalignment angle for train SINS/GNSS integrated navigation system based on an observability analysis. When the train enters a straight-line segment, the alignment of the train’s sideslip angle and the satellite velocity heading angle allows the achievement of velocity heading observation values that resolve the issue. In a curved segment, the heading angle becomes observable, allowing for an accurate estimation of the SINS’s heading misalignment angle using GNSS observations. The results showed that, whether the train is on a straight or curved track, the position estimation accuracy meets the simulation design criteria of 0.1 m, and the heading accuracy is better than 0.25°. In comparison to the results of pure GNSS position and velocity-assisted navigation, where heading divergence occurs during constant velocity straight-line segments, the method proposed in this paper not only converges but also achieves an accuracy comparable to the GNSS velocity-based heading alignment. The simulation results demonstrate that the proposed strategy significantly improves the accuracy of the heading misalignment angle estimation, thereby enhancing the accuracy of speed and position estimation under a GNSS-denied environment.

Optimization of direct injection mixture formation for dual-fuel low speed marine engine using novel compound electric gas injection devices

For two-stroke low speed marine engines, using a natural gas-diesel dual-fuel mode can effectively reduce emissions such as NOX, SOX, and CO2, making it an important approach to promote the low-carbon and environmentally friendly development of the shipping industry. To address the issues of abnormal combustion and gas leakage faced by low-pressure gas direct injection, a novel compound electric gas injection device is proposed, which is designed with a coaxial arrangement of a moving coil actuator and a moving iron actuator. Based on this new device, a three-dimensional computational fluid dynamics (CFD) model of the low speed marine engine operating process is established to simulate the distribution characteristics of the in-cylinder flow field under 16 different scenarios, including different injection side-slip angles and lower deflection angles, and to clarify the impact of these factors on the formation, diffusion, and evolution of the mixture. Based on this, an evaluation system was established from two dimensions: mixture uniformity and gas leakage rate. An improved multi-attribute decision-making TOPSIS algorithm based on the entropy weight method was used to define the attribute importance using information entropy, and to construct an optimization evaluation model for the spatial arrangement of the injection device. The optimal arrangement angle was calculated, in which a side-slip angle was 20° and a lower deflection angle was 10°. This method avoids the problem of unreasonable decisions caused by subjective factors such as designer preferences and experience, effectively improving the in-cylinder mixture uniformity and reducing gas leakage rate.

An Adaptive Line-of-Sight (ALOS) Guidance Law for Path Following of Aircraft and Marine Craft

This brief presents a novel nonlinear adaptive line-of-sight (ALOS) guidance law for path following, compensating for drift forces due to wind, waves, and ocean currents. The ALOS guidance law is proven to have uniform semiglobal exponential stability (USGES) properties during straight-line path following at constant speed. The ALOS guidance law performs similar to the classical integral line-of-sight (ILOS) and adaptive ILOS guidance laws when the sideslip angle is nearly constant. The ALOS guidance law, however, has better tracking capabilities when compensating for rapidly varying sideslip caused by a time-varying disturbance. This is because the integral state of the ALOS guidance law is additive to the unknown sideslip angle (disturbance matching). In contrast, the ILOS guidance laws must compensate sideslip through a saturating arctangent function. The study also includes an input-to-state stable (ISS) reduced-order extended state observer for estimation of the line-of-sight (LOS) crab angle, known as the ELOS guidance law. The performance of the ALOS, ILOS, and ELOS guidance laws is compared by simulating rapid changes in the sideslip angle to stress the critical assumptions of the algorithms. Finally, a case study of the Remus 100 autonomous underwater vehicle (AUV) exposed to stochastic ocean currents is used to compare the performance of the ILOS, ALOS, and ELOS algorithms during normal operation.

Vehicle Stability Analysis by Zero Dynamics to Improve Control Performance

Good stability performance is a prerequisite for the motion of vehicles controlled either by a human driver with advanced driver-assistance systems or by highly automated driving systems. Hence, vehicle motion control has become the core technology for vehicles driving everywhere in all conditions. However, vehicles might start to skid or spin, i.e., lose motion stability, even if some vehicle motion controllers are active. This motivates our work with a twofold contribution. First, we show that there are zero dynamics when input–output linearization is used in controller design. By analyzing the behavior of the zero dynamics, we identify why and under what conditions the vehicle starts to slide sideways and then spins. Second, based on the analysis results, we propose a cooperative control scheme to improve the motion stability of vehicles. A linear combination of the yaw rate and the sideslip angle is used as the control output, and we discuss which linear combinations are appropriate. Finally, simulation results, including a hardware-in-the-loop simulation, and experimental results with a refitted DongFeng E70 vehicle are given to illustrate the effectiveness of our method.

Experimental studies of airliner aerodynamic characteristics at overcritical angles of attack

PurposeThe main purpose of this study was to determine the basic aerodynamic characteristics of the airliner Tu-154M at the wide range of the overcritical angles of attack and sideslip angles, i.e. α = −900° ÷ 900° and β = −900° ÷ 900°.Design/methodology/approachWind tunnel tests of the Tu-154M aircraft model at the scale 1:20 were performed in a low-speed wind tunnel T-3 by using a six-component internal aerodynamic balance. Several model configurations were also investigated.FindingsThe results of the presented studies showed that at the wide range of the overcritical angles of attack and sideslip angles, i.e. α = −900° ÷ 900° and β = −900° ÷ 900°, the Tu-154M aircraft flap deflection affected the values of the drag and lift coefficients and generally had no major effect on the values of the side force and pitching moment coefficients.Research limitations/implicationsThe model vibration which was the result of flow separation at high angles of attack was the wind tunnel test limitation.Practical implicationsStudies of the airliner aerodynamic characteristics at the wide range of the overcritical angles of attack and sideslip angles allow assessment of the aircraft aerodynamic properties during possible unexpected situations when the passenger aircraft is found to have gone beyond the conventional flight envelope.Social implicationsThere are no social implications of this study to report.Originality/valueThe presented wind tunnel test results of the airliner aerodynamic characteristics at overcritical angles of attack and sideslip angles is an original contribution to the existing not-too-extensive database available in the literature.

Vehicle state and parameter estimation based on improved extend Kalman filter

In order to reduce the influence of historical measurement data errors in the process of vehicle state estimation and improve the accuracy of the vehicle state estimation, a limited memory random weighted extended Kalman filter (LMRWEKF) algorithm is proposed. Firstly, a 3-DOF nonlinear vehicle dynamics model is established. Secondly, the limited memory extended Kalman filter is formed by fusing the limited memory filter and the extended Kalman filter. Then, according to the random weighting theory, the weighting coefficients that obey Dirichlet distribution are introduced to further improve the filtering estimation accuracy. Finally, a virtual test based on the ADAMS/CAR is used for the experimental verification. The results show that the error in the longitudinal velocity and the yaw rate is small, especially in the mean value of estimation error of side slip angle which is different in just 0.015 degrees between the virtual test and the simulation result. And also, the results compared with traditional methods indicate that the proposed LMRWEKF algorithm can solve the problem of vehicle state estimation with the performance of noise fluctuation suppression and higher estimation accuracy. The mean absolute error (MAE) and root mean square error (RMSE) are considered to verify the estimation accuracy of the proposed algorithm. And the comparison results indicate that the estimation accuracy of the LMRWEKF algorithm is significantly higher than those of the EKF and DEKF methods.

Improved path following for autonomous marine vehicles with low-cost heading/course sensors: comparative experiments

By overcoming constraints of low-cost sensors equipped on the autonomous marine vehicle (AMV), the classic path following approaches based on heading autopilot and course autopilot are improved to enhance the following accuracy of AMV, respectively. For the heading autopilot, adaptive line-of-sight (ALOS) is proposed to compensate for the generalized sideslip including the actual sideslip angle and the magnetic compass bias. For the course autopilot, extended Kalman filter (EKF) is employed to fusion the position and yaw angular rate to obtain the more precise and stable course, so as to substitute the unreliable course feedback from the satellite navigation receiver in cases of low speed and non ideal positioning. The performance and characteristics of both improved schemes are summarized through comparative experiments conducted on a low-cost AMV. Compared with the classic path following method, the straight line path following accuracy of the heading autopilot with generalized sideslip compensation and the course autopilot with EKF feedback are improved by 78.3% and 85.8% respectively. The circular path following accuracy are improved by 61.8% and 66.7% respectively. Engineers can choose the path following methods flexibly based on the hardware configuration, working environment and path type of the AMV to improve the following accuracy.

Vehicle Dynamics in Electric Cars Development Using MSC Adams and Artificial Neural Network

Recently, there has been renewed interest in lightweight structures; however, a small structure change can strongly affect vehicle dynamic behavior. Therefore, this study provides new insights into non-parametric modeling based on artificial neural networks (ANNs). This work is then motivated by the requirement for a reliable substitute for virtual instrumentation in electric car development to enable the prediction of the current value of the vehicle slip from a given time history of the vehicle (input) and previous values of synthetic data (feedback). The training data are generated from a multi-body simulation using MSC Adams Car; the simulation involves a double lane-change maneuver. This test is commonly used to evaluate vehicle stability. Based on dynamic considerations, this study implements the nonlinear autoregressive exogenous (NARX) identification scheme used in time-series modeling. This work presents an ANN that is able to predict the side slip angle from simulated training data generated employing MSC Adams Car. This work is a specific solution to overtake maneuvers, avoiding the loss of vehicle control and increasing driving safety.

Research on path following control system of wave gliders based on maneuverability demand estimator

The wave glider (WG) with a propeller-rudder system represents a novel kind of unmanned surface vehicle that has a hybrid driving capability of wave energy and solar energy, compared to the WG with only a rudder system. The propeller-rudder system can improve the flexibility and maneuverability of the WG, especially in low-speed and nearshore operations. However, owing to the limited solar power generation and high energy consumption of the propeller, studying the precise control switching strategy of the propeller is of great significance. In this paper, a path following control system is proposed for the WG with a propeller-rudder system based on an adaptive line-of-sight algorithm (ALOS), a maneuverability demand estimator and a maneuverability controller. The maneuverability demand estimator is determined by the cross-track error and sideslip angle. And the cross-track error and sideslip angle are fused by using fuzzy mathematics to establish the mobility demand estimation model. The maneuverability demand index (MDI) intuitively expresses the urgency of the mobility demand during the path following process and is input to the maneuverability controller to control the propeller revolution. Through numerical simulations and sea trials, the effectiveness and superiority of the proposed path-following control system is verified.

DYC Design for Autonomous Distributed Drive Electric Vehicle Considering Tire Nonlinear Mechanical Characteristics in the PWA Form

This paper presents a novel direct yaw moment control (DYC) strategy for autonomous distributed drive electric vehicle (DDEV) to improve path-following driving stability under critical maneuvers. Due to the fact that the tire mechanical characteristics show highly nonlinear under those critical conditions, the piecewise affine (PWA) identification method is chosen in this work to model the tire nonlinear mechanical characteristics since it shows best model accuracy/simplicity compromise for system control design. On this basis, to properly reflect the coupling behaviors between longitudinal motion and lateral motion, the vehicle dynamics model which considers the tire nonlinear mechanical characteristics in the PWA form is established. By analyzing the phase plane of vehicle nonlinear model, a novel external yaw moment calculation method based on global fast terminal sliding mode (GFTSM) control method is applied to the upper layer of DYC architecture for eliminating the errors of yaw rate and sideslip angle. Then, different weight coefficients are allocated to obtain the final external yaw moment. In the lower controller, the optimal four-wheel torque is distributed according to a concise and accurate algorithm in which adaptive weight coefficients are considered. The simulation results verify the effectiveness of the proposed control strategy in improving the driving stability performance of autonomous DDEV against two other control strategies under three extreme conditions.

Vehicle Sideslip Angle Estimation Based on Radial Basis Neural Network and Unscented Kalman Filter Algorithm

Most existing ESC (electronic stability control) and ADS (auto drive system) stability controls rely on the measurement of yaw rate and sideslip angle. However, the existing sensors are too expensive, which is one of the factors that makes it difficult to measure the side slip angle of vehicles directly. Therefore, the estimation of sideslip angle has been extensively discussed in the relevant literature. Accurate modeling is complicated by the fact that vehicles are highly nonlinear. This article combines a radial basis function neural network with an unscented Kalman filter to propose a new sideslip angle estimation method for controlling the dynamic behavior of vehicles. Considering the influence of input data type and sensor ease of measurement factors on the results, a two-degrees-of-freedom vehicle nonlinear dynamic model was established, and a radial basis function neural network estimation algorithm was designed. In order to reduce the impact of noise and improve the reliability of the algorithm, the neural network algorithm was combined with the Kalman filter. The information collected from low-cost sensors for actual vehicle operation (longitudinal vehicle speed, steering wheel angle, yaw rate, lateral acceleration) was trained using a radial basis function neural network to obtain a “pseudo slip angle”. The “pseudo slip angle”, yaw rate, and lateral acceleration are input as observations of the Kalman filter. The sideslip angle obtained from different observation methods was compared with the values provided by the Carsim 2020. The experiment shows that the sideslip angle estimator based on the radial basis function neural network and unscented Kalman filter achieves the optimal effect.

Recurrent Neural Network Model for On-Board Estimation of the Side-Slip Angle in a Four-Wheel Drive and Steering Vehicle

<div>A valuable quantity for analyzing the lateral dynamics of road vehicles is the side-slip angle, that is, the angle between the vehicle’s longitudinal axis and its speed direction. A reliable real-time side-slip angle value enables several features, such as stability controls, identification of understeer and oversteer conditions, estimation of lateral forces during cornering, or tire grip and wear estimation. Since the direct measurement of this variable can only be done with complex and expensive devices, it is worth trying to estimate it through virtual sensors based on mathematical models. This article illustrates a methodology for real-time on-board estimation of the side-slip angle through a machine learning model (SSE—side-slip estimator). It exploits a recurrent neural network trained and tested via on-road experimental data acquisition. In particular, the machine learning model only uses input signals from a standard road car sensor configuration. The model adaptability to different road conditions and tire wear levels has been verified through a sensitivity analysis and model testing on real-world data proves the robustness and accuracy of the proposed solution achieving a root mean square error (RMSE) of 0.18 deg and a maximum absolute error of 1.52 deg on the test dataset. The proposed model can be considered as a reliable and cheap potential solution for the real-time on-board side-slip angle estimation in serial cars.</div>

AFS control system research of distributed drive electric vehicles by adaptive super-twisting sliding mode control

To solve the problems of serious buffeting in traditional sliding mode control and difficulty in obtaining the derivative information of the system sliding mode surface, a distributed drive electric vehicles active front steering (AFS) control method based on adaptive super-twisting sliding mode control (ASTSMC) is proposed. Taking the yaw rate deviation as the state quantity, the stable and convergent sliding mode surface is designed to obtain the equivalent control input of the front wheel angle. The sliding mode function information is substituted into the parameters of the super-twisting algorithm; the discontinuous term is kept in the integrand function to keep the control signal continuous and weaken the system chattering; the adaptive control law is added to design the AFS controller. The co-simulation results of Matlab/Simulink and Carsim show that ASTSMC can reduce the yaw rate by 40.59% compared with no control under step steering condition. Compared with the sliding mode controller, ASTSMC has optimized the sideslip angle by 5.41% under the double line shifting condition.

Yaw Moment Control Based on Brake-by-Wire for Vehicle Stbility

This paper presents a new control strategy for vehicle stability based on brake-by-wire. However, there are few studies in the literature that compare the stability of a vehicle by systematic experimentation with or without controllers. In this paper, the complete experimental procedure is designed, and the experimental results are analyzed in detail. Firstly, the hydraulic model of the brake-by-wire is established based on its structure and working principles, and the yaw moment control method is proposed for the vehicle’s stability. The deviation between the desired values and actual values of the yaw rate and sideslip angle is taken as the input, and the fuzzy controller calculates the additional yaw moment for the vehicle stability. Next, the simulation under different conditions which contain the steering wheel step input, double lane change and turning is conducted, and the yaw rates and sideslip angles with and without stability control are compared, and the effectiveness of the control method is verified. Finally, the turning test is conducted based on brake-by-wire chassis to verify the proposed method. The experimental results show that the yaw rate decreased by 14% and the sideslip angle decreased by 25% when the brake control was applied. Furthermore, the proposed method performed well in improving the stability of the brake-by-wire chassis.

State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Kalman Filter

As a new type of transportation, the distributed drive electric vehicle is regarded as the main development direction of electric vehicles in the future. Due to the advantages of the independently controllable driving torque of each wheel, it provides more favorable conditions for vehicle active safety control. Acquiring accurate and real-time parameters such as vehicle speed and side slip angle is a prerequisite for vehicle active safety control. Therefore, relying on the National Natural Science Foundation of China, this paper takes the distributed drive electric vehicle in the form of four-wheel independent drive and steering as the research object. Taking the measurement data of low-cost vehicle sensors as input and adaptive Kalman filtering as theoretical support, the sub-filter of federal Kalman filtering adds a fuzzy controller on the basis of volumetric Kalman filtering, and designs the vehicle driving state estimation algorithm to realize the accurate estimation of driving state information. Finally, the typical experimental conditions are selected, and the designed algorithm is verified by the co-simulation of MATLAB/Simulink and CarSim. At the same time, the algorithm is further verified based on the driving simulator hardware-in-the-loop experimental platform. The results show that the designed estimation algorithm has good effects in terms of accuracy, stability, and real-time performance.

Assessment of Total Pressure and Swirl Distortions in a Busemann Inlet at Mach 6

Flow distortions in high-speed inlet systems are complex, and high-performance air-breathing propulsion systems. In this paper, large eddy simulations are performed to study the total pressure and swirl distortions in a Busemann inlet at freestream Mach number 6. The on-design flow condition with both the Attack Angle and Sideslip Angle equal to zero and two off-design conditions (Attack Angle = 6 deg, Sideslip Angle = 0 deg and Attack Angle = 6 deg, Sideslip Angle = 6 deg) are considered to explore the flow characteristics inside the inlet duct as well as the distortions at the inlet exit plane. It is found that under the on-design flow condition, the shock structures and boundary layer development are nearly axisymmetric about the inlet axis. The captured freestream is compressed smoothly through inlet duct. The total pressure loss is limited primarily to within the boundary layer region, and nearly no swirling flow is introduced during the flow compression process. Under the off-design flow conditions, the shock structures inside the inlet duct become non-axisymmetric, and localized strong shock–boundary layer interactions occur. In the case of the off-design flow condition with Attack Angle = 6 deg, Sideslip Angle = 0 deg, a large flow separation zone appears owing to the incidence of a strong curved shock on the wall surface at the leeward side in the inlet duct, and the low-kinetic-energy flow contained in this flow separation zone leads to an obvious total-pressure reduction at the exit plane of inlet. Meanwhile, a large-scale swirling flow is formed at the exit plane of inlet owing to the appearance of a nonuniform transverse pressure gradient. Under the off-design conditions, a pair of vortex is observed at the exit plane of inlet. The shock wave–boundary layer interactions under the off-design conditions are stronger than those under the on-design condition, which results in more intense total pressure and swirl distortions. The averages of the fluctuating distortions are more evident than the temporal-averaged total-pressure and swirl distortions. These results show that turbulent flow fluctuations are important in determining the overall distortion level in a Busemann inlet.

An Enabling Tire-Road Friction Estimation Method for Four-in-Wheel-Motor-Drive Electric Vehicles

In this paper, a particle filter (PF)-based tire-road friction estimation method is proposed for four-in-wheel-motor-drive electric vehicles by synthetically using the Dual Global Positioning System (DGPS) and three low-cost Inertia Measurement Units (IMUs). In the scheme, two independent PF-based road friction estimators are developed for straight driving and cornering conditions. For straight driving conditions, the longitudinal tire forces are first estimated using the output torque and rotational speed of motor, based on which a tire-road friction estimator is put forward by using a particle filter and the nonlinear relationship between longitudinal tire force and tire-road friction. For cornering conditions, the lateral tire forces and vehicle sideslip angle are estimated by using three IMUs and the DGPS. A PF-based tire-road friction estimator is established based on the nonlinear lateral tire characteristics. An estimation mode decision scheme is developed to determine which of the two PF-based estimators is used to update the tire-road friction estimate by considering both tire dynamics states and tire force characteristics. The accuracy and reliability of the proposed tire-road friction estimation scheme is verified under various maneuvers and road friction conditions through hardware-in-the-loop tests. The results show that the proposed method exhibits high estimation accuracy, robustness, and computational efficiency.

Stability Analysis of Lane-Keeping Assistance System for Trucks under Crosswind Conditions

To enhance the control accuracy of lane-keeping assistance systems for trucks encountering crosswind-induced lateral deviations to improve the lateral stability of the vehicle, this study proposes a control strategy based on a linear quadratic regulator (LQR) using a path-tracking preview model. First, the lateral deviation is calculated using the path-tracking preview model. Then, an observer for the vehicle’s sideslip angle is designed using a vehicle lateral tracking deviation model and a Kalman filter controller, and this is used to solve the deviation of the sideslip angle. Finally, a feedforward controller is designed based on the LQR controller and a linear two-degrees-of-freedom vehicle model to eliminate steady-state errors arising from LQR optimization, thereby obtaining the steering angle of the vehicle when subjected to crosswind conditions. Comparing the test results of the sideslip angle, yaw rate, and lateral acceleration demonstrates that this strategy effectively improves the control accuracy of lane-keeping under crosswind conditions. The proposed method is validated through hardware-in-the-loop experiments on a test bench, yielding results consistent with simulations.

Airborne four channel fiber coupled vector laser Doppler anemometer system.

Using an airborne vector laser Doppler anemometer (LDA) to measure the air flow outside of the boundary layer of an airplane is a promising optical technique. Measurement of the primary flight data like true airspeed, the angle of attack, and the angle of sideslip can be directly derived from the measured wind vector. We developed an experimental system with interchangeable telescopes to study the change in the LDA sensitivity and signal rate, depending on the focusing of the measurement beam. The system has a real-time-capable field programmable gate array data acquisition system, which also can record full data dumps for off-line analysis. This paper presents the first results of an airborne measurement campaign. The true airspeed is measured with an residual error of 1% compared to the five-hole flow sensor. The angle of sideslip and the angle of attack show a standard deviation of the residual error of 0.6∘ for the angle of sideslip and 0.2∘ for the angle of attack.