Vehicle Steering Stability Simulation Based on G-Vectoring Control

2014 ◽  
Vol 898 ◽  
pp. 914-918
Author(s):  
Yun Yin Zhang ◽  
Chun Guang Liu ◽  
Zi Li Liao
Keyword(s):  
Vehicle Dynamics ◽  
Driving Force ◽  
Control Method ◽  
Stability Control ◽  
Lateral Acceleration ◽  
System Control ◽  
Dynamics Model ◽  
Sideslip Angle ◽  
Vehicle System ◽  
Simulation Based

A new kind of control method named "G-Vectoring control" is used in vehicle steering stability control, which uses the lateral acceleration to control the longitudinal acceleration, and improves the steering stability by redistributing the driving force. The motor and its control system as well as the vehicle system control are modeled by Matlab, the vehicle dynamics model is designed by adams. After the co-simulation of snakelike tests, the results shows that the sideslip angle is well controlled by G-Vectoring control.

Technology Research Vehicle Braking Stability Control

2014 ◽  
Vol 602-605 ◽  
pp. 1219-1222
Author(s):  
Ya Rong Liu
Keyword(s):  
Vehicle Dynamics ◽  
Fuzzy Controller ◽  
Control Variable ◽  
Stability Control ◽  
Technology Research ◽  
Dynamics Model ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
The Stability ◽  
And Control

In this paper, the stability of the car when braking, the establishment of a complete vehicle dynamics model to analyze the main causes and influencing factors of automobile brake instability. Select the vehicle yaw rate and sideslip angle as the control variable, meaning the use of certain applications of fuzzy control theory, the ESP fuzzy controller, and control strategy simulation with.

scholarly journals Stability Control for Electric Vehicles with Four In-Wheel-Motors Based on Sideslip Angle

2021 ◽  
Vol 12 (1) ◽  
pp. 42
Author(s):  
Kun Yang ◽  
Danxiu Dong ◽  
Chao Ma ◽  
Zhaoxian Tian ◽  
Yile Chang ◽  
...  
Keyword(s):  
Electric Vehicles ◽  
Control Method ◽  
Sliding Mode ◽  
Stability Control ◽  
Torque Control ◽  
Wheel Load ◽  
Sideslip Angle ◽  
Distribution Method ◽  
Load Rate ◽  
The Stability

Tire longitudinal forces of electrics vehicle with four in-wheel-motors can be adjusted independently. This provides advantages for its stability control. In this paper, an electric vehicle with four in-wheel-motors is taken as the research object. Considering key factors such as vehicle velocity and road adhesion coefficient, the criterion of vehicle stability is studied, based on phase plane of sideslip angle and sideslip-angle rate. To solve the problem that the sideslip angle of vehicles is difficult to measure, an algorithm for estimating the sideslip angle based on extended Kalman filter is designed. The control method for vehicle yaw moment based on sliding-mode control and the distribution method for wheel driving/braking torque are proposed. The distribution method takes the minimum sum of the square for wheel load rate as the optimization objective. Based on Matlab/Simulink and Carsim, a cosimulation model for the stability control of electric vehicles with four in-wheel-motors is built. The accuracy of the proposed stability criterion, the algorithm for estimating the sideslip angle and the wheel torque control method are verified. The relevant research can provide some reference for the development of the stability control for electric vehicles with four in-wheel-motors.

Model Predictive Controller Design for Vehicle Motion Control at Handling Limits in Multiple Equilibria on Varying Road Surfaces

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6667
Author(s):  
Szilárd Czibere ◽  
Ádám Domina ◽  
Ádám Bárdos ◽  
Zsolt Szalay
Keyword(s):  
Steady State ◽  
Vehicle Dynamics ◽  
Multiple Equilibria ◽  
Stability Control ◽  
Equilibrium Analysis ◽  
Stable Region ◽  
Test Scenario ◽  
Sideslip Angle ◽  
Road Surfaces ◽  
Vehicle Motion

Electronic vehicle dynamics systems are expected to evolve in the future as more and more automobile manufacturers mark fully automated vehicles as their main path of development. State-of-the-art electronic stability control programs aim to limit the vehicle motion within the stable region of the vehicle dynamics, thereby preventing drifting. On the contrary, in this paper, the authors suggest its use as an optimal cornering technique in emergency situations and on certain road conditions. Achieving the automated initiation and stabilization of vehicle drift motion (also known as powerslide) on varying road surfaces means a high level of controllability over the vehicle. This article proposes a novel approach to realize automated vehicle drifting in multiple operation points on different road surfaces. A three-state nonlinear vehicle and tire model was selected for control-oriented purposes. Model predictive control (MPC) was chosen with an online updating strategy to initiate and maintain the drift even in changing conditions. Parameter identification was conducted on a test vehicle. Equilibrium analysis was a key tool to identify steady-state drift states, and successive linearization was used as an updating strategy. The authors show that the proposed controller is capable of initiating and maintaining steady-state drifting. In the first test scenario, the reaching of a single drifting equilibrium point with −27.5° sideslip angle and 10 m/s longitudinal speed is presented, which resulted in −20° roadwheel angle. In the second demonstration, the setpoints were altered across three different operating points with sideslip angles ranging from −27.5° to −35°. In the third test case, a wet to dry road transition is presented with 0.8 and 0.95 road grip values, respectively.

scholarly journals Sliding Mode Control of Laterally Interconnected Air Suspensions

2020 ◽  
Vol 10 (12) ◽  
pp. 4320 ◽  
Author(s):  
Dou Guowei ◽  
Yu Wenhao ◽  
Li Zhongxing ◽  
Amir Khajepour ◽  
Tan Senqi
Keyword(s):  
Ride Comfort ◽  
Control Method ◽  
Sliding Mode ◽  
Suspension System ◽  
Lateral Acceleration ◽  
Sliding Mode Controller ◽  
The Road ◽  
Air Suspension ◽  
Simulation Based ◽  
Different Levels

This paper presents a control method based the lateral interconnected air suspension system, in order to improve the road handling of vehicles. A seven-DOF (Degree of freedom) full-vehicle model has been developed, which considers the features of the interconnected air suspension system, for example, the modeling of the interconnected pipelines and valves by considering the throttling and hysteresis effects. On the basis of the well-developed model, a sliding mode controller has been designed, with a focus on constraining and minimizing the roll motion of the sprung mass caused by the road excitations or lateral acceleration of the vehicle. Moreover, reasonable road excitations have been generated for the simulation based on the coherence of right and left parts of the road. Afterwards, different simulations have been done by applying both bumpy and random road excitations with different levels of roughness and varying vehicle lateral accelerations. The simulation results indicate that the interconnected air suspension without control can improve the ride comfort, but worsen the road handling performance in many cases. However, by applying the proposed sliding mode controller, the road handling of the sprung mass can be improved by 20% to 85% compared with the interconnected or non-interconnected mode at a little cost of comfort.

The Application of the Elman Network on the Vehicle Handling Stability Control

2011 ◽  
Vol 199-200 ◽  
pp. 1457-1461 ◽  
Author(s):  
Si Jia Zhou ◽  
Jiang Qi Long ◽  
Ke Gang Zhao
Keyword(s):  
Driving Force ◽  
Control Method ◽  
Control Strategies ◽  
Stability Control ◽  
Vehicle Stability ◽  
Elman Network ◽  
Vehicle Handling ◽  
Combined Control ◽  
One Step ◽  
Tracking Response

In this paper, an expanded Elman network is applied to forecast the vehicle dynamic characteristic and a one step predictive control is also put into use to reinforce its handling stability. The combined control strategy is established based on the conception of the distribution of the driving force between the front and rear driving axles that can be easily achieved in an EV. Moreover, in this research, the distribution proportion of longitudinal driving force defining as a parameter is introduced and the control method of vehicle stability with the aid of the distribution proportion between axles is investigated．Simu1ations have been carried out and the results indicate that the proposed control strategies achieve smooth control effects and rapid target tracking response. This method can be easily applied to the vehicles that are driven by motors, and is capable of improving the lateral dynamic stability of vehicles in most conditions．

scholarly journals A Robust Control Method for Lateral Stability Control of In-Wheel Motored Electric Vehicle Based on Sideslip Angle Observer

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Yaxiong Wang ◽  
Feng Kang ◽  
Taipeng Wang ◽  
Hongbin Ren
Keyword(s):  
Robust Control ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Control Method ◽  
Stability Control ◽  
Lateral Stability ◽  
Level Control ◽  
Sideslip Angle ◽  
Safety Control ◽  
Yaw Moment

In-wheel motored powertrain on electric vehicles has more potential in maneuverability and active safety control. This paper investigates the longitudinal and lateral integrated control through the active front steering and yaw moment control systems considering the saturation characteristics of tire forces. To obtain the vehicle sideslip angle of mass center, the virtual lateral tire force sensors are designed based on the unscented Kalman filtering (UKF). And the sideslip angle is estimated by using the dynamics-based approaches. Moreover, based on the estimated vehicle state information, an upper level control system by using robust control theory is proposed to specify a desired yaw moment and correction front steering angle to work on the electric vehicles. The robustness of proposed algorithm is also analyzed. The wheel torques are distributed optimally by the wheel torque distribution control algorithm. Numerical simulation is carried out in Matlab/Simulink-Carsim cosimulation environment to demonstrate the effectiveness of the designed robust control algorithm for lateral stability control of in-wheel motored vehicle.

Ranking Tires for Heavy Truck Steering Feel Performance Using a Simulation Model2

2006 ◽  
Vol 34 (1) ◽  
pp. 64-82 ◽  
Author(s):  
S. L. Haas
Keyword(s):  
Vehicle Dynamics ◽  
Performance Metrics ◽  
Steering System ◽  
Lateral Acceleration ◽  
Steering Wheel ◽  
Dynamics Model ◽  
Objective Testing ◽  
Steering Feel ◽  
Wheel Torque ◽  
Heavy Truck

Abstract The effects of seven different tire sets on heavy truck steering feel characteristics were demonstrated from objective testing. Also, the steering behavior and vehicle dynamics were modeled in order to determine how well the resulting simulations could rank the steering performance of the tire sets relative to the objective results. The objective testing was performed using a 6×4 tractor with a two-axle flatbed semi-trailer. Measured data included steering wheel torque, steering wheel angle, and lateral acceleration behavior resulting from on-center-type steering tests. In addition, the hydraulic pressure from the power steering system was also measured. The tests consisted of multiple cycles at 0.2 Hz and ±0.2 g. Steering-related performance metrics were selected and calculated based on the interaction between measured parameters. The same test procedure was also applied using an analytical model of a steering system. The input was steering wheel torque, and outputs included the road wheel angles at the steer axle, which were then fed into a commercial vehicle dynamics model providing the vehicle dynamics behavior along with feedback required for the steering model (e.g., king pin moments). Tire loads and slip angles were also provided by the vehicle dynamics model and used as input to a tire model predicting tire force and moment behavior. The related metrics were subsequently computed and compared to the measured results. Effects of the different tire sets on steering characteristics were seen from both the objective and simulation tests. Seven performance metrics were applied in a ranking comparison between measured and modeled results. Correlation of the modeled to measured metrics ranged from R2 values of 0.40 to 0.99 for the seven metrics considered.

A Fuzzy Synthesis Control Scheme and Optimization for Vehicle Dynamic Stability System

2007 ◽  
Author(s):  
Yunqing Zhang ◽  
Si Gao ◽  
Lingyang Li ◽  
Liping Chen ◽  
Jingzhou Yang ◽  
...  
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
High Speed ◽  
Control Method ◽  
Stability Control ◽  
Surface Model ◽  
Vehicle Stability ◽  
Control Parameters ◽  
Vehicle Dynamic ◽  
Sideslip Angle

Vehicle stability control system can enhance the vehicle stability and handling in the emergency situations through the control of traction and braking forces at the individual wheels. This paper presents a Fuzzy synthesis control strategy with an ideal 2-DOF linear model and optimization of the control parameters. The control strategy consists of Fuzzy control of two control objectives (yaw velocity ω and sideslip angle β). Fuzzy functions can adjust and control these two objectives and through Matlab Fuzzy control unit & ADAMS multi-body vehicle dynamic model we obtain optimized simulation. The co-simulation scenario is on iced road with a single sine steering angle input and in a high speed. The control parameters are optimized and analyzed by a combined optimization algorithm (Genetic Algorithm (GA) and Nonlinear Programming Quadratic Line search (NLPQL) method) combined with response surface model (RSM). The simulation results show that the handling stability and safety of the vehicle can be enhanced by the Fuzzy control method that can adapt complex road and driving conditions.

scholarly journals Vehicle Sideslip Angle Estimation Based on General Regression Neural Network

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Wang Wei ◽  
Bei Shaoyi ◽  
Zhang Lanchun ◽  
Zhu Kai ◽  
Wang Yongzhi ◽  
...  
Keyword(s):  
Neural Network ◽  
Estimation Method ◽  
Stability Control ◽  
Lateral Acceleration ◽  
General Regression Neural Network ◽  
Slip Angle ◽  
Sideslip Angle ◽  
Training Samples ◽  
Stability Control System ◽  
General Regression

Aiming at the accuracy of estimation of vehicle’s mass center sideslip angle, an estimation method of slip angle based on general regression neural network (GRNN) and driver-vehicle closed-loop system has been proposed: regarding vehicle’s sideslip angle as time series mapping of yaw speed and lateral acceleration; using homogeneous design project to optimize the training samples; building the mapping relationship among sideslip angle, yaw speed, and lateral acceleration; at the same time, using experimental method to measure vehicle’s sideslip angle to verify validity of this method. Estimation results of neural network and real vehicle experiment show the same changing tendency. The mean of error is within 10% of test result’s amplitude. Results show GRNN can estimate vehicle’s sideslip angle correctly. It can offer a reference to the application of vehicle’s stability control system on vehicle’s state estimation.

Research on Vehicle Stability Based on DYC and AFS Integrated Controller

2013 ◽  
Vol 278-280 ◽  
pp. 1510-1515 ◽  
Author(s):  
Jie Tian ◽  
Ya Qin Wang ◽  
Ning Chen
Keyword(s):  
Control Method ◽  
Sliding Mode ◽  
Stability Control ◽  
Front Wheel ◽  
Vehicle Stability ◽  
Sideslip Angle ◽  
Stable Domain ◽  
Vehicle Stability Control ◽  
Integrated Controller ◽  
Yaw Moment

A new vehicle stability control method integrated direct yaw moment control (DYC) with active front wheel steering (AFS) was proposed. On the basis of the vehicle nonlinear model, vehicle stable domain was determined by the phase plane of sideslip angle and sideslip angular velocity. When the vehicle was outside the stable domain, DYC was firstly used to produce direct yaw moment, which can make vehicle inside the stable domain. Then AFS sliding mode control was used to make the sideslip angle and yaw rate track the reference vehicle model. The simulation results show that the integrated controller improves vehicle stability more effectively than using the AFS controller alone.

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