scholarly journals Vehicle Sideslip Angle Estimation Using Two Single-Antenna GPS Receivers

2010 ◽  
Author(s):  
Jong-Hwa Yoon ◽  
Huei Peng
Keyword(s):  
Low Cost ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
Safety Systems

Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Control (ESC). Vehicle sideslip angle can be measured through optical speed sensors, or dual-antenna GPS. These measurement systems are costly (∼$5k to $100k), which prohibits wide adoption of such systems. This paper demonstrates that the vehicle sideslip angle can be estimated in real-time by using two low-cost single-antenna GPS receivers. Fast sampled signals from an Inertial Measurement Unit (IMU) compensate for the slow update rate of the GPS receivers through an Extended Kalman Filter (EKF). Bias errors of the IMU measurements are estimated through an EKF to improve the sideslip estimation accuracy. A key challenge of the proposed method lies in the synchronization of the two GPS receivers, which is achieved through an extrapolated update method. Analysis reveals that the estimation accuracy of the proposed method relies mainly on vehicle yaw rate and longitudinal velocity. Experimental results confirm the feasibility of the proposed method.

Study on the Possibility to Estimate the Vehicle Side Slip Using Two Independent GPS Receivers

2016 ◽  
Vol 822 ◽  
pp. 321-330
Author(s):  
Dinu Covaciu ◽  
Ion Preda ◽  
Dragoş Sorin Dima ◽  
Anghel Chiru
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Vehicle Body ◽  
Slip Angle ◽  
Electronic Stability Program ◽  
Sideslip Angle ◽  
Gps Receivers ◽  
Single Antenna ◽  
Active Safety Systems ◽  
The Difference

Slip angle is the difference between the direction a vehicle is travelling and the longitudinal plane of the vehicle body. Knowing vehicle sideslip angle accurately is critical for active safety systems such as Electronic Stability Program (ESP). Vehicle sideslip angle can be measured using optical speed sensors, inertial sensors and/or dual-antenna GPS receivers. These systems are expensive and their use is limited for many users. The goal of this paper is to analyze the possibility to estimate the vehicle sideslip angle, in real-time, by using two low-cost single-antenna GPS receivers.

scholarly journals On the Tripped Rollovers and Lateral Skid in Three-Wheeled Vehicles and Their Mitigation

Vehicles ◽  
2021 ◽  
Vol 3 (3) ◽  
pp. 357-376
Author(s):  
Martín-Antonio Rodríguez-Licea
Keyword(s):  
Soft Soil ◽  
Stability Control ◽  
Measurement Unit ◽  
Common Lyapunov Function ◽  
Problem Resolution ◽  
Active Safety Systems ◽  
Propulsion Control ◽  
Main Components ◽  
Safety Systems ◽  
Wheeled Vehicles

Active safety systems for three-wheeled vehicles seem to be in premature development; in particular, delta types, also known as tuk-tuks or sidecars, are sold with minimal protection against accidents. Unfortunately, the risk of wheel lifting and lateral and/or longitudinal vehicle roll is high. For instance, a tripped rollover occurs when a vehicle slides sideways, digging its tires into soft soil or striking an object. Unfortunately, research is mostly aimed at un-tripped rollovers while most of the rollovers are tripped. In this paper, models for lateral skid tripped and un-tripped rollover risks are presented. Later, independent braking and accelerating control actions are used to develop a dynamic stability control (DSC) to assist the driver in mitigating such risks, including holes/bumps road-scenarios. A common Lyapunov function and an LMI problem resolution ensure robust stability while optimization allows tuning the controller. Numerical and HIL tests are presented. Implementation on a three-wheeled vehicle requires an inertial measurement unit, and independent ABS and propulsion control as main components.

scholarly journals A Novel Comprehensive Scheme for Vehicle State Estimation Using Dual Extended H-Infinity Kalman Filter

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1526
Author(s):  
Fengjiao Zhang ◽  
Yan Wang ◽  
Jingyu Hu ◽  
Guodong Yin ◽  
Song Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Estimation Accuracy ◽  
Vehicle Speed ◽  
Sideslip Angle ◽  
H Infinity ◽  
Yaw Rate ◽  
Cost Constraints ◽  
Active Safety Systems ◽  
Vehicle State Estimation

The performance of vehicle active safety systems relies on accurate vehicle state information. Estimation of vehicle state based on onboard sensors has been popular in research due to technical and cost constraints. Although many experts and scholars have made a lot of research efforts for vehicle state estimation, studies that simultaneously consider the effects of noise uncertainty and model parameter perturbation have rarely been reported. In this paper, a comprehensive scheme using dual Extended H-infinity Kalman Filter (EH∞KF) is proposed to estimate vehicle speed, yaw rate, and sideslip angle. A three-degree-of-freedom vehicle dynamics model is first established. Based on the model, the first EH∞KF estimator is used to identify the mass of the vehicle. Simultaneously, the second EH∞KF estimator uses the result of the first estimator to predict the vehicle speed, yaw rate, and sideslip angle. Finally, simulation tests are carried out to demonstrate the effectiveness of the proposed method. The test results indicate that the proposed method has higher estimation accuracy than the extended Kalman filter.

Robust sideslip angle observer of commercial vehicles based on cornering stiffness estimation using neural network

2022 ◽  
pp. 095440702110724
Author(s):  
Chenyu Zhou ◽  
Liangyao Yu ◽  
Yong Li ◽  
Jian Song
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Stability Control ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Commercial Vehicles ◽  
Sideslip Angle ◽  
The Neural Network ◽  
Wide Range ◽  
The Mathematical Model

Accurate estimation of sideslip angle is essential for vehicle stability control. For commercial vehicles, the estimation of sideslip angle is challenging due to severe load transfer and tire nonlinearity. This paper presents a robust sideslip angle observer of commercial vehicles based on identification of tire cornering stiffness. Since tire cornering stiffness of commercial vehicles is greatly affected by tire force and road adhesion coefficient, it cannot be treated as a constant. To estimate the cornering stiffness in real time, the neural network model constructed by Levenberg-Marquardt backpropagation (LMBP) algorithm is employed. LMBP is a fast convergent supervised learning algorithm, which combines the steepest descent method and gauss-newton method, and is widely used in system parameter estimation. LMBP does not rely on the mathematical model of the actual system when building the neural network. Therefore, when the mathematical model is difficult to establish, LMBP can play a very good role. Considering the complexity of tire modeling, this study adopted LMBP algorithm to estimate tire cornering stiffness, which have simplified the tire model and improved the estimation accuracy. Combined with neural network, A time-varying Kalman filter (TVKF) is designed to observe the sideslip angle of commercial vehicles. To validate the feasibility of the proposed estimation algorithm, multiple driving maneuvers under different road surface friction have been carried out. The test results show that the proposed method has better accuracy than the existing algorithm, and it’s robust over a wide range of driving conditions.

scholarly journals Attitude Determination Method by Fusing Single Antenna GPS and Low Cost MEMS Sensors Using Intelligent Kalman Filter Algorithm

2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Lei Wang ◽  
Bo Song ◽  
Xueshuai Han ◽  
Yongping Hao
Keyword(s):  
Kalman Filter ◽  
Attitude Determination ◽  
Fuzzy Controller ◽  
Low Cost ◽  
Transition Process ◽  
Measurement Unit ◽  
Accurate Estimation ◽  
Mems Sensors ◽  
Fusion Algorithm ◽  
Single Antenna

For meeting the demands of cost and size for micronavigation system, a combined attitude determination approach with sensor fusion algorithm and intelligent Kalman filter (IKF) on low cost Micro-Electro-Mechanical System (MEMS) gyroscope, accelerometer, and magnetometer and single antenna Global Positioning System (GPS) is proposed. The effective calibration method is performed to compensate the effect of errors in low cost MEMS Inertial Measurement Unit (IMU). The different control strategies fusing the MEMS multisensors are designed. The yaw angle fusing gyroscope, accelerometer, and magnetometer algorithm is estimated accurately under GPS failure and unavailable sideslip situations. For resolving robust control and characters of the uncertain noise statistics influence, the high gain scale of IKF is adjusted by fuzzy controller in the transition process and steady state to achieve faster convergence and accurate estimation. The experiments comparing different MEMS sensors and fusion algorithms are implemented to verify the validity of the proposed approach.

scholarly journals A Novel Roll and Pitch Estimation Approach for a Ground Vehicle Stability Improvement Using a Low Cost IMU

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 340 ◽  
Author(s):  
Malik Kamal Mazhar ◽  
Muhammad Jawad Khan ◽  
Aamer Iqbal Bhatti ◽  
Noman Naseer
Keyword(s):  
Kalman Filters ◽  
Sliding Mode ◽  
Low Cost ◽  
Warning Signal ◽  
Stability Control ◽  
Attitude Estimation ◽  
Roll Angle ◽  
Measurement Unit ◽  
Second Phase ◽  
Ground Vehicle

Onboard attitude estimation for a ground vehicle is persuaded by its application in active anti-roll bar design. Conventionally, the attitude estimation problem for a ground vehicle is a complex one, and computationally, its solution is very intensive. Lateral load transfer is an important parameter which should be taken in account for all roll stability control systems. This parameter is directly related to vehicle roll angle, which can be measured using devices such as dual antenna global positioning system (GPS) which is a costly technique, and this led to the current work in which we developed a simple and robust attitude estimation technique that is tested on a ground vehicle for roll mitigation. In the first phase Luenberger and Sliding mode observer is implemented using simplest roll dynamics model to measure the roll angle of a vehicle and the validation of results is carried using commercial software, CarSim® (CarSim, Ann Arbor, MI, USA). In the second phase of research, complementary and Kalman filters have been designed for attitude estimation. In the third phase, a low-cost inertial measurement unit (IMU) is mounted on a vehicle, and both the complementary filter (CF) and Kalman filter (KF) are applied independently to measure the data for both smooth and uneven terrains at four different frequencies. We compared the simulated and real-time results of roll and pitch angles obtained using the complementary and Kalman filters. Using the proposed method, the achieved root mean square error (RMSE) is less than 0.73 degree for pitch and 0.68 degree for roll, with a sample time of 2 ms. Thus, a warning signal can be generated to mitigate roll over. Hence, we claim that our proposed method can provide a low-cost solution to the roll-over problem for a road vehicle.

Vehicle handling and stability control by the cooperative control of 4WS and DYC

2017 ◽  
Vol 31 (19-21) ◽  
pp. 1740090 ◽  
Author(s):  
Huan Shen ◽  
Yun-Sheng Tan
Keyword(s):  
Control System ◽  
Cooperative Control ◽  
Sliding Mode ◽  
Integrated Control ◽  
Longitudinal Velocity ◽  
Stability Control ◽  
Sideslip Angle ◽  
Vehicle Handling ◽  
Braking Torque ◽  
Yaw Moment

This paper proposes an integrated control system that cooperates with the four-wheel steering (4WS) and direct yaw moment control (DYC) to improve the vehicle handling and stability. The design works of the four-wheel steering and DYC control are based on sliding mode control. The integration control system produces the suitable 4WS angle and corrective yaw moment so that the vehicle tracks the desired yaw rate and sideslip angle. Considering the change of the vehicle longitudinal velocity that means the comfort of driving conditions, both the driving torque and braking torque are used to generate the corrective yaw moment. Simulation results show the effectiveness of the proposed control algorithm.

Study of Vehicle Sideslip Angle Real-Time Estimation Method

2013 ◽  
Vol 846-847 ◽  
pp. 26-29
Author(s):  
Xiao Bin Fan ◽  
Pan Deng
Keyword(s):  
Real Time ◽  
Estimation Method ◽  
Time Estimation ◽  
Stability Control ◽  
Estimation Methods ◽  
Estimation Accuracy ◽  
Slip Angle ◽  
Sideslip Angle ◽  
Direct Integration Method ◽  
Real Time Estimation

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.

Enhanced Full-State Estimation and Dynamic-Model-Based Prediction for Road-Vehicles

2014 ◽  
Author(s):  
Aliakbar Alamdari ◽  
Javad Sovizi ◽  
Venkat N. Krovi
Keyword(s):  
State Estimation ◽  
Unscented Kalman Filter ◽  
Low Cost ◽  
Nonlinear Differential Equations ◽  
Predictive Performance ◽  
Stability Control ◽  
High Fidelity ◽  
Electronic Stability Program ◽  
Uneven Terrain ◽  
Safety Systems

In this paper, we address the enhanced state estimation and prediction system for automobile applications by fusing relatively low-cost and noisy Inertial Navigation System (INS) sensing with Global Positioning System (GPS) measurements. An unscented Kalman filter is used to merge multi-rate measurements from GPS and INS sensors together with a high-fidelity vehicle-dynamics model for state-predictions. The high-fidelity motion model (including suspension-effects) for the vehicle motion trajectory on uneven terrain is captured by a 20-state system of nonlinear differential equations. Computer simulation results illustrate the effectiveness of sensor-fusion (building upon the merger of an inexpensive INS sensing with GPS based measurements) to accurately estimate the full system-state. The relative ease of implementation, accuracy and predictive performance with low-cost sensing will facilitate its use in various electronic control and safety-systems, such as Electronic Stability Program, Anti-lock Brake Systems, and the Lateral Dynamic Stability Control.

Optimized Processing in IMU-Aided GPS Signal Tracking

2012 ◽  
Vol 157-158 ◽  
pp. 62-65
Author(s):  
Lin Zhao ◽  
Yong Hao ◽  
Li Shu Guo
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Moving Average ◽  
Dynamic Environment ◽  
Doppler Frequency ◽  
Search Space ◽  
Estimation Accuracy ◽  
Measurement Unit ◽  
Signal Tracking ◽  
Local Code

A GPS signal tracking method utilizing optimized processing in inertial measurement unit (IMU) aided GPS receiver is studied. In order to enhance the sensitivity of baseband processing, low-cost inertial sensors are applied in the GPS and strapdown inertial navigation system (SINS) integration commonly, where the estimation accuracy of Doppler frequency would influence the whole performance of the tracking loops. Stochastic errors of carrier Doppler estimation caused by inertial sensors and local oscillators are corrected utilizing auto regressive moving average (ARMA) method in this paper. And then the accuracy Doppler information is used to correct the local code phase and local carrier frequency to further determine the search space of frequency domain which is unlike to the design of traditional aided loop. Simulation results indicate that the bandwidth of carrier loop and code loop could be decreased significantly in high dynamic environment.

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