Sideslip Angle Measurement Research Articles

Estimating vehicle sideslip angle through kinematic and dynamic contributions: Theory and experimental results

Vehicle lateral stability plays an important role within vehicle passenger safety. The study of lateral stability is typically related to investigating the dynamics of relevant vehicle states: among these, the vehicle sideslip angle ([Formula: see text]) emerges as a prominent candidate. Sideslip angle measurement is expensive and impractical, hence estimation techniques are often used, typically based on Kalman filters or neural networks, both with their issues. This work presents an alternative estimation method based on the idea of splitting sideslip angle into kinematic and dynamic contributions, and by observing that the kinematic contribution is straightforward to estimate. Therefore, efforts are devoted into estimating dynamic sideslip angle, which is herein obtained through a parametric interpolation harnessing lateral acceleration. Only data available from traditional vehicle onboard sensors are used in the process. Experimental results are presented along several manoeuvres on a full-scale vehicle, with the estimator running online within a dSPACE unit, ultimately supporting the efficacy and real-time feasibility of the proposed approach.

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.

Multi-objective H2/H∞ saturated non-PDC static output feedback control for path tracking of autonomous vehicle

This paper presents a new fuzzy output feedback control design for autonomous vehicle steering under actuator saturation, unavailability of the sideslip angle measurement, unknown road curvature, and lateral wind force. To take into account the actuator constraint, the saturation effect is transformed into dead-zone nonlinearity. A static output controller based on non-compensation parallel distributed technic and a Takagi-Sugeno (T-S) model of vehicle lateral dynamics is proposed to consider the unavailability of some vehicle states. To avoid the problem of imposing bounds on membership functions time derivatives resulting from the use of the fuzzy Lyapunov approach, a proper integral structure based on the non-quadratic Lyapunov approach is investigated. The mixed [Formula: see text] stabilization conditions of the augmented closed-loop system are expressed in terms of linear matrix inequalities (LMIs). Finally, the robustness and the advantages of the proposed approaches are demonstrated through different tests.

Soft Measurement of Intelligent Vehicle side-slip Angle Based on Extended Kalman Filter*

A soft measurement method based on extended Kalman filter is proposed for the measurement of Intelligent Vehicle CG Side-slip Angle. Firstly, the nonlinear dynamic state model of intelligent vehicle is established, the state equation of Side-slip Angle is determined, and the extended Kalman soft measurement method is used for analysis. Secondly,through the establishment of a real vehicle test system, the soft measurement method of Side-slip Angle is verified under two working conditions and different vehicle speeds on the actual road. The test results show that the extended Kalman soft measurement method is reliable and accurate. The extended Kalman soft measurement method can meet the requirements of the full vehicle control system, and it has a certain value of full vehicle application research.

Vehicle sideslip estimation via kernel-based LPV identification: Theory and experiments

Many vehicle control systems depend on the body sideslip angle, but robust and cost-effective direct measurement of this angle is yet to be achieved for production vehicles. Estimation from indirect measurements is thus the only viable option. In the paper, a sideslip estimator is obtained through the identification of a linear parameter varying (LPV) model. Although inspired by physical insights into the vehicle lateral dynamics, the structure of the LPV estimator is not parametrized beforehand. Instead, the estimator is learned by means of a state-of-the-art non-parametric method for linear parameter varying identification, namely least-squares support vector machines (LS-SVM). Its performance is assessed over an extensive and heterogeneous set of experimental data, showing the effectiveness of the proposed estimator.

Fuzzy Adaptive Attitude Estimation for a Fixed-Wing UAV With a Virtual SSA Sensor During a GPS Outage

This paper proposes a novel robust attitude estimation algorithm for a small unmanned aerial vehicle (UAV) in the absence of GPS measurements. A synthetic sideslip angle (SSA) measurement formulated for use under the zeroangle assumption is newly proposed for a UAV without angle-of-attack (AOA)/SSA sensors to enhance the state estimation performance during a GPS outage. In addition, the nongravitational acceleration is estimated using the proposed Kalman filter and is then subtracted from the raw acceleration to yield a reliable gravity estimate. Then, a fuzzy-logic-aided adaptive measurement covariance matching algorithm is devised to adaptively reduce the weight given to disturbed acceleration and magnetic field measurements in the attitude estimation, yielding the fuzzy adaptive error-state Kalman filter (FAESKF) algorithm. Experimental flight results demonstrate that the proposed FAESKF algorithm achieves a remarkable improvement in attitude estimation compared to the conventional algorithm.

Estimation and Simulation Analysis of the Sideslip Angle of Intelligent Vehicle under Unstructured Road Environment

Vehicle sideslip angle is one of the important variables to describe the vehicle's lateral motion state, and it is also difficult to estimate accurately. In order to solve the difficult problem of direct measurement of intelligent vehicle sideslip angle in unstructured road environment, a 3-DOF vehicle Dynamics model was established, Carsim and Matlab were also used to construct the parametric model of vehicle. Based on the extended Kalman filter (EKF) algorithm, the State observer was used to estimate the sideslip angle of the intelligent vehicle. In the case of emergency shift, the estimation effect of State observer is verified by joint simulation. The simulation results show that the proposed method can accurately estimate the sideslip angle of the intelligent vehicle.

An Adaptive Approach to Real-Time Estimation of Vehicle Sideslip, Road Bank Angles, and Sensor Bias

Robust estimation of vehicle sideslip angle is essential for stability control applications. However, the direct measurement of sideslip angle is expensive for production vehicles. This paper presents a novel sideslip estimation algorithm which relies only on sensors available on passenger and commercial vehicles. The proposed method uses both kinematics and dynamics vehicle models to construct extended Kalman filter observers. The estimate relies on the results provided from the dynamics model observer where the tire cornering stiffness parameters are updated using the information provided from the kinematics model observer. The stability property of the proposed algorithm is discussed and proven. Finally, multiple experimental tests are conducted to verify its performance in practice. The results show that the proposed approach provides smooth and accurate sideslip angle estimation. In addition, our novel algorithm provides reliable estimates of bank angles and lateral acceleration sensor bias.

Running States Estimation of Autonomous Four-Wheel Independent Drive Electric Vehicle by Virtual Longitudinal Force Sensors

Exact sideslip angle estimation is significant to the dynamics control of four-wheel independent drive electric vehicles. It is costly and difficult-to-popularize to equip vehicular sensors for real-time sideslip angle measurement; therefore, the reliable sideslip angle estimation method is investigated in this paper. The electric driving wheel model is proposed and applied to the longitudinal force estimation. Considering that electric driving wheel model is a nonlinear model with unknown input, an unknown input estimation method is proposed to facilitate the longitudinal force observer design, in which the adaptive high-order sliding mode observer is designed to achieve the state estimation, the analytic formula of longitudinal force is obtained by decoupling electric driving wheel model, and the longitudinal force estimator is designed by recurrence estimation method. With the designed virtual longitudinal force sensor, an adaptive attenuated Kalman filtering is proposed to estimate the vehicle running state, in which the time-varying attenuation factor is applied to weaken the past data to the current filter and the covariance of process noise and measurement noise can be adjusted adaptively. Finally, simulations and experiments are conducted and the effectiveness of proposed estimation method is validated.

Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an Unscented Kalman Filter algorithm

Most existing ESC (Electronic Stability Control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in the relevant literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. In this paper, we propose a novel observer based on ANFIS, combined with Kalman Filters in order to estimate the sideslip angle, which in turn is used to control the vehicle dynamics and improve its behavior. For this reason, low-cost sensor measurements which are integrated into the actual vehicle and executed in real time have to be used. The ANFIS system estimates a “pseudo-sideslip angle” through parameters which are easily measured, using sensors equipped in actual vehicles (inertial sensors and steering wheel sensors); this value is introduced in UKF in order to filter noise and to minimize the variance of the estimation mean square error. The estimator has been validated by comparing the observed proposal with the values provided by the CARSIM model, which is a piece of experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle, by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS+UKF-based sideslip angle estimator.

Digital image correlation techniques for measuring tyre-road interface parameters: Part 1 – Side-slip angle measurement on rough terrain

This paper presents inexpensive methods whereby the vehicle side-slip angle can be measured accurately at low speeds on any terrain using cameras. Most commercial side-slip angle sensor systems and estimation techniques rely on smooth terrain and high vehicle speeds, typically above 20km/h, to provide accurate measurements. However, during certain in-situ tyre and vehicle testing on off-road conditions, the vehicle may be travelling at speeds slower than required for current sensors and estimation techniques to provide sufficiently accurate results. Terramechanics tests are typical case in point. Three algorithms capable of determining the side-slip angle from overlapping images are presented. The first is a simple fast planar method. The second is a more complex algorithm which can extract not only the side-slip angle but also its rotational velocities and scaled translational velocities. The last uses a calibrated stereo-rig to obtain all rotations and translational movement in world coordinates. The last two methods are aimed more at rough terrain applications, where the terrain induces motion components other than typical predominant yaw-plane motion. The study however found no discernible difference in measured side-slip angle of the methods. The system allows for accurate measurement at low and higher speeds depending on camera speed and lighting.

Sideslip angle estimator based on ANFIS for vehicle handling and stability

Most of the existing ESC (Electronic stability control) systems rely on the measurement of both yaw rate and sideslip angle. However, one of the main issues is that the sideslip angle cannot be measured directly because the sensors are too expensive. For this reason, sideslip angle estimation has been widely discussed in literature. The modeling of sideslip angle is complex due to the non-linear dynamics of the vehicle. This work proposes a new methodology based on ANFIS to estimate the vehicle sideslip angle. The estimator has been validated by comparing the proposed ANFIS prediction model with the values provided by CARSIM model, which is an experimentally validated software. The advantage of this estimation is the modeling of the non-linear dynamics of the vehicle by means of signals which are directly measured from vehicle sensors. The results show the effectiveness of the proposed ANFIS-based sideslip angle estimator.

Vehicle side-slip angle estimation based on MHE suppressing the outlier of inertial sensors

The accuracy of side-slip angle and yaw rate measurements is important in vehicle motion control. However, vehicle side-slip angle measurement is difficult. In general, side-slip angle can be estimated by observer. In this paper, we propose a vehicle state estimation method based on Moving Horizon Estimation (MHE) using acceleration sensor and gyro sensor. Measurement data of inertial sensor has large noise, since S/N ratio is small, especially if estimation object is a light weight vehicle. Moreover, outlier of yaw rate measurement value is caused by road surface disturbance. MHE is used for vehicle state estimation that can suppress the measurement noise on inertial sensors. In this paper, we show the efficacy of the proposed method through the simulation.

Observer-Based Robust Control of Vehicle Dynamics for Rollover Mitigation in Critical Situations

This paper describes a vehicle dynamics fuzzy control design to improve stability and minimize the rollover risk of the vehicle in critical situations. To obtain a robust controller, several aspects that directly affect the behavior of the vehicle have been considered. Nonlinearities of the lateral forces have been considered by using a Takagi-Sugeno (TS) representation, changes in road friction have been taken into account by introducing parameter uncertainties, and, finally, the road bank angle is considered as an unknown input in the used vehicle dynamics model. A TS observer has been proposed and designed with unmeasurable premise variables in order to consider the unavailability of the sideslip angle measurement. The observer and controller gains are simultaneously obtained by solving the proposed linear matrix inequalities constraints. A fishhook test is conducted in a CarSim simulator in order to illustrate the performance of the designed controller.

Study of Vehicle Sideslip Angle Real-Time Estimation Method

In the vehicle stability control and other active safety systems, vehicle sideslip angle real-time estimation is necessary. However, the direct measurement of sideslip angle is more difficult or too costly, so it is often used in estimating methods. The vehicle sideslip angle of closed-loop Luenberger observer and Kalman observer were constructed based on two degrees of freedom bicycle model, as well as the direct integration method for large sideslip angle conditions. The comparative study showed that Kalman filtering estimation method and Luenberger estimation methods have better estimation accuracy in small slip angle range.

Measurement and Analysis of Side-Slip Angle for an Articulated Vehicle

Experiments to measure the side-slip angle of an articulated vehicle were conducted using a wheel loader equipped with displacement, global positioning system (GPS), and fibre-optic gyro (FOG) sensors. In addition, a constant circular turn of the articulated vehicle in a horizontal plane was simulated using equations of motion for a vehicle with no tractive force. The cornering stiffness between tire and unpaved ground was determined by comparing the experimental and simulation results.

Measurement and Analysis of Side-Slip Angle for an Articulated Vehicle

Measurement and Analysis of Side-Slip Angle for an Articulated Vehicle

ACTIVE FRONT STEERING DURING BRAKING PROCESS

An active front steering(AFS) intervention control during braking for vehicle stability is presented. Based on the investigation of AFS mechanism, a simplified model of steering system is established and integrated with vehicle model. Then the AFS control on vehicle handling dynamics during braking is designed. Due to the difficulties associated with the sideslip angle measurement of vehicle, a state observer is designed to provide real time estimation. Thereafter, the controller with the feedback of both sideslip and yaw angle is implemented. To evaluate the system control, the proposed AFS controlled vehicle has been tested in the Hardware-in-the-loop-simulation (HILS) system and compared with that of conventional vehicle. Results show that AFS can improve vehicle lateral stability effectively without reducing the braking performance.

A methodology for vehicle sideslip angle identification: comparison with experimental data

Sideslip angle could provide important information concerning vehicle's stability. Unfortunately direct measurement of sideslip angle requires a complex and expensive experimental set-up, which is not suitable for implementation on ordinary passenger cars; thus, this quantity has to be estimated starting from the measurements of vehicle lateral/longitudinal acceleration, speed, yaw rate and steer angle. According to the proposed methodology, sideslip angle is estimated as a weighted mean of the results provided by a kinematic formulation and those obtained through a state observer based on vehicle single-track model. Kinematical formula is considered reliable for a transient manoeuvre, while the state observer is used in nearly quasi-state condition. The basic idea of the work is to make use of the information provided by the kinematic formulation during a transient manoeuvre to update the single-track model parameters (tires cornering stiffnesses). A fuzzy-logic procedure was implemented to identify steady state or transient conditions.

SIDESLIP ANGLE MEASUREMENT, EXPERIMENTAL CHARACTERIZATION AND EVALUATION OF THREE DIFFERENT PRINCIPLES

The knowledge of the sideslip angle is fundamental for control of the vehicle's lateral dynamics. In order to measure this angle, there are essentially three different principles: GPS/INS and optical correlation as used by available sensors and model-based techniques like observers. This paper focuses on the two following items. First, as the sensors are too expensive to be mounted on common vehicles, we investigate a low cost solution, based on two different observers, a common bicycle observer and a new observer including roll dynamics. Secondly, as the experimental validation phase of these observers needs the knowledge of reference measurements, we confront GPS/INS measurement principle and optical correlation, by characterization of two sensors: RT3002 and Correvit S400 in order to monitor their advantages and drawbacks. This characterization is carried out with a specific test bench and the different sideslip angle measurement solutions are compared with real tests performed with our laboratory experimental car.