Analysis of the vehicle driving stability region based on the bifurcation of the driving torque and the steering angle

2017 ◽  
Vol 231 (7) ◽  
pp. 984-998 ◽  
Author(s):  
Shuming Shi ◽  
Ling Li ◽  
Xianbin Wang ◽  
Hongfei Liu ◽  
Yuqiong Wang
Keyword(s):  
Vehicle Dynamics ◽  
Stability Region ◽  
Integrated Control ◽  
Steering Angle ◽  
Vehicle Handling ◽  
Vehicle System ◽  
Non Linear ◽  
Driving Stability ◽  
Driving Torque ◽  
Dynamics Stability

Integrated control systems for vehicle-handling stability are usually based on the steering bifurcation mechanism. The best integrated control performance is obtained by coordinating different control methods. However, in vehicle steering and driving conditions, the coupling characteristics of the longitudinal forces and the lateral forces of the tyres must lead to changes in the bifurcation characteristics. The corresponding vehicle dynamics stability region has to be redetermined. The corresponding integrated control method also needs to be adjusted. Therefore, in combination with the physical significance of the dynamics equilibrium point of the vehicle system, the definition of the driving stability region of the vehicle based on the characteristics of the driving torque and the steering angle bifurcation is proposed. With the concept presented above, the five-degree-of-freedom non-linear vehicle system model for the driving stability region of the vehicle was solved. The simulation results show that, according to the driving stability region of the vehicle, the vehicle dynamics stability with different driving torque inputs and different front-wheel steering-angle inputs can be accurately estimated. The study of the driving stability region of the vehicle is beneficial for engineering applications in non-linear automotive dynamics research. In addition, it provides the theoretical basis for integrated control of the vehicle-handling stability.

Vehicle coupled bifurcation analysis of steering angle and driving torque

2021 ◽  
pp. 095440702098540
Author(s):  
Xianbin Wang ◽  
Shuming Shi
Keyword(s):  
Bifurcation Analysis ◽  
Hybrid Method ◽  
Vehicle Dynamics ◽  
Front Wheel ◽  
Steering Angle ◽  
Driving Mode ◽  
Vehicle System ◽  
Autonomous Equation ◽  
Real Coded Genetic Algorithm ◽  
Driving Torque

The mechanism of vehicle dynamics steering bifurcation has almost been confirmed. But the present steering bifurcation mechanism cannot explain the bifurcation phenomena caused by the driving torque. As a result, the vehicle coupled bifurcation analysis of the steering angle and driving torque has not been studied. Based on the five degrees of freedom (5DOF) vehicle system dynamics model with driving torque involved, the vehicle dynamics equilibriums under different driving torque and driving mode were searched by a hybrid method in this paper. The hybrid method combined the real-coded Genetic Algorithm with Quasi-Newton gradient method. According to the definition of static bifurcation of nonlinear systems, the equilibrium bifurcation of 5DOF vehicle system was confirmed. Then, the 5DOF vehicle system model was transformed into autonomous equation with the front wheel steering angle as intermediate variable. From the two aspects of constant steering angle amplitude and constant driving torque, the bifurcation diagrams of different driving mode were calculated. The vehicle coupled bifurcation characteristics of steering angle and driving torque were analyzed. The results show that the values of the driving torque will directly affect the bifurcation characteristics of vehicle dynamics system. The coupled feature of the front wheel steering angle and driving torque effect on vehicle bifurcation is obvious.

Research for the Turning Vehicles ESP Control Performances on the Yaw Elastic Restriction Vehicle System

2012 ◽  
Vol 229-231 ◽  
pp. 325-330
Author(s):  
Guo Ye Wang ◽  
Lu Zhang ◽  
Guo Yan Chen ◽  
Zhong Fu Zhang
Keyword(s):  
Vehicle Dynamics ◽  
Stability Control ◽  
Dynamics Simulation ◽  
Control Performance ◽  
Vehicle System ◽  
Test Conditions ◽  
Vehicle Systems ◽  
Set Up ◽  
The Stability ◽  
Driving Stability

Project the structure of the yaw elastic restriction vehicle system, and set up the system dynamic model. Establish yaw elastic restriction vehicle dynamics simulation system based on Matlab/Simulink aimed at Chery A3 sedan. Adopting the brake driving integration ESP control strategy, analyze and verify the stability control performance of independent vehicle systems and yaw elastic restriction vehicle system respectively in neutral steer, understeer and oversteer three test conditions. The results of the study show that the stability control performance of yaw elastic restriction vehicle system and independent vehicle systems has remarkable consistency. This provides a basis for vehicle driving stability control test.

Integrated vehicle dynamics control using semi-active suspension and active braking systems

2017 ◽  
Vol 232 (3) ◽  
pp. 314-329 ◽  
Author(s):  
Abbas Soltani ◽  
Ahmad Bagheri ◽  
Shahram Azadi
Keyword(s):  
Vehicle Dynamics ◽  
Ride Comfort ◽  
Unscented Kalman Filter ◽  
Sliding Mode ◽  
Active Suspension ◽  
Stability Control ◽  
Linear Estimator ◽  
Vehicle Handling ◽  
The Road ◽  
Non Linear

This article presents an integrated control of yaw, roll and vertical dynamics based on a semi-active suspension and an electronic stability control with active differential braking system. During extreme manoeuvres, the probability of vehicle rollover is increased and the stability of lateral and yaw vehicle motions is deteriorated because of the saturation of tyre forces. Furthermore, when the road excitation frequencies are equal to the natural frequencies of the unsprung masses, the resonance phenomena occurs, which causes some oscillations getting revealed on responses of the yaw and lateral vehicle dynamics. In these situations, the active braking alone cannot be helpful to improve the vehicle handling and stability, considerably. In order to overcome these difficulties, a coordinated control of the semi-active suspension and the active braking is proposed, using a fuzzy controller and an adaptive sliding mode controller, respectively. A non-linear full vehicle model with 14 degrees of freedom is established and combined with the modified Pacejka tyre model. As the majority of vehicle dynamics variables and the road profile inputs cannot be measured in a cost-efficient way, a non-linear estimator based on unscented Kalman filter is designed to estimate the entire vehicle dynamics states and the road unevenness. Simulation results of the steering manoeuvres on the random road inputs show that the proposed chassis system can effectively improve the vehicle handling, stability and ride comfort.

Comparison of Analytical and Empirical Models to Capture Variations in Off-Road Vehicle Dynamics

2005 ◽  
Author(s):  
Paul J. Pearson ◽  
David M. Bevly
Keyword(s):  
Empirical Data ◽  
Vehicle Dynamics ◽  
Experimental Validation ◽  
Inertial Sensors ◽  
Analytical Models ◽  
Control Algorithms ◽  
Model Parameters ◽  
Steering Control ◽  
Steering Angle ◽  
Yaw Rate

This paper develops two analytical models that describe the yaw dynamics of a farm tractor and can be used to design or improve steering control algorithms for the tractor. These models are verified against empirical data. The particular dynamics described are the motions from steering angle to yaw rate. A John Deere 8420 tractor, outfitted with inertial sensors and controlled through a PC-104 form factor computer, was used for experimental validation. Conditions including different implements at varying depths, as would normally be found on a farm, were tested. This paper presents the development of the analytical models, validates them against empirical data, and gives trends on how the model parameters change for different configurations.

The Integrated Control of Semi-Active Suspension and Electronic Stability Program Based on Fuzzy Logic Method

2010 ◽  
Vol 29-32 ◽  
pp. 2059-2064
Author(s):  
Jian Hua Guo ◽  
Liang Chu ◽  
Xiao Bing Zhang ◽  
Fei Kun Zhou
Keyword(s):  
Fuzzy Logic ◽  
Integrated Control ◽  
Integrated System ◽  
Ride Quality ◽  
Electronic Stability Program ◽  
Vehicle Handling ◽  
Logic Control ◽  
Fuzzy Logic Method ◽  
The Individual ◽  
Integrated Control System

In this paper, an integrated system of SAS/ESP is proposed to improve vehicle handling performance and stability. The 15DOF vehicle model which describes the dynamics of the integrated system is established. A fuzzy logic control strategy is presented to control the integrated system. The simulation results show that the integrated control system can obviously improve vehicle maneuverability and ride quality much more than the individual control.

Integrated Vehicle Dynamics Control Via Torque Vectoring Differential and Electronic Stability Control to Improve Vehicle Handling and Stability Performance

2018 ◽  
Vol 140 (7) ◽  
Author(s):  
Seyed Mohammad Mehdi Jaafari ◽  
Kourosh Heidari Shirazi
Keyword(s):  
Vehicle Dynamics ◽  
Phase Plane ◽  
Stability Control ◽  
Stable Region ◽  
Electronic Stability Control ◽  
Vehicle Handling ◽  
Matlab Simulink ◽  
Β Phase ◽  
Torque Vectoring ◽  
Chassis Control

This paper proposed a full vehicle state estimation and developed an integrated chassis control by coordinating electronic stability control (ESC) and torque vectoring differential (TVD) systems to improve vehicle handling and stability in all conditions without any interference. For this purpose, an integrated TVD/ESC chassis system has been modeled in Matlab/Simulink and applied into the vehicle dynamics model of the 2003 Ford Expedition in carsim software. TVD is used to improve handling in routine and steady-state driving conditions and ESC is mainly used as the stability controller for emergency maneuvers or when the TVD cannot improve vehicle handling. By the β−β˙ phase plane, vehicle stable region is determined. Inside the reference region, the handling performance and outside the region the vehicle stability has been in question. In order to control the integrated chassis system, a unified controller with three control layers based on fuzzy control strategy, β−β˙ phase plane, longitudinal slip, and road friction coefficient of each tire is designed in Matlab/Simulink. To detect the control parameters, a state estimator is developed based on unscented Kalman filter (UKF). Bees algorithm (BA) is employed to optimize the fuzzy controller. The performance and robustness of the integrated chassis system and designed controller were conformed through routine and extensive simulations. The simulation results via a co-simulation of MATLAB/Simulink and CarSim indicated that the designed integrated ESC/TVD chassis control system could effectively improve handling and stability in all conditions without any interference between subsystems.

Fourier Series-Based Neural Networks for Vehicle System Identification

1999 ◽  
Author(s):  
Imtiaz Haque ◽  
Juergen Schuller
Keyword(s):  
Neural Networks ◽  
Fourier Series ◽  
System Identification ◽  
Transfer Function ◽  
Parameter Identification ◽  
Frequency Domain ◽  
Vehicle System ◽  
Non Linear ◽  
Function Identification ◽  
Non Linear System

Abstract The use of neural networks in system identification is an emerging field. Neural networks have become popular in recent years as a means to identify linear and non-linear systems whose characteristics are unknown. The success of sigmoidal networks in parameter identification has been limited. However, harmonic activation-based neural networks, recent arrivals in the field of neural networks, have shown excellent promise in linear and non-linear system parameter identification. They have been shown to have excellent generalization capability, computational parallelism, absence of local minima, and good convergence properties. They can be used in the time and frequency domain. This paper presents the application of a special class of such networks, namely Fourier Series neural networks (FSNN) to vehicle system identification. In this paper, the applications of the FSNNs are limited to the frequency domain. Two examples are presented. The results of the identification are based on simulation data. The first example demonstrates the transfer function identification of a two-degree-of freedom lateral dynamics model of an automobile. The second example involves transfer function identification for a quarter car model. The network set-up for such identification is described. The results of the network identification are compared with theory. The results indicate excellent prediction properties of such networks.

Sign in / Sign up

Export Citation Format

Share Document