A Study of Vehicle Dynamics Stability Based on Fuzzy Control

2011 ◽  
Vol 403-408 ◽  
pp. 5107-5111
Author(s):  
Chang Gao Xia ◽  
Ji Lei Wang
Keyword(s):  
Mathematical Model ◽  
Fuzzy Control ◽  
Vehicle Dynamics ◽  
Reference Model ◽  
Stability Control ◽  
Time Varying ◽  
Electronic Stability Program ◽  
Sideslip Angle ◽  
Yaw Rate ◽  
Stability Control System

Electronic Stability Program (ESP) has become the focus of the study in the field of automotive active safety and chassis control in recent years, which was developed from ABS and TCS. ESP mainly works through adjusting the size and the distribution of the longitudinal tire force. ESP can make vehicle produce effective yaw moment to restrain oversteering or understeering, However, ESP is a typical nonlinear, time-delay, time-varying parameter system and its mathematical model is very complex. It is difficult to design the control model by traditional control theory. Fuzzy control does not depend on a precise mathematical model. It is employed to handle complicated questions of nonlinear dynamics. First, in this paper, the7-DOF of vehicle dynamics model based on the H. B. Pacejka tyre model (magic formula) and vehicle reference model were established by using the MATLAB/SIMULINK. Then by using fuzzy control principle to direct at the nonlinear, time varying characteristics of the ESP system, a controller of yaw rate based on fuzzy control was designed. An analysis of the simulation results of J-turn and lane change on slippery road surface shows that the present stability control system based on the yaw rate is effective in maintaining the yaw rate and the sideslip angle within the optimal range, thus improving the vehicle stability.

Vehicle Direct Yaw Moment Control with Longitudinal Forces Distribution

2014 ◽  
Vol 709 ◽  
pp. 331-334
Author(s):  
Man Hong Huang ◽  
Huan Shen ◽  
Yun Sheng Tan
Keyword(s):  
Control System ◽  
Reference Model ◽  
Sliding Mode ◽  
Stability Control ◽  
Yaw Rate ◽  
Vehicle Stability Control ◽  
Braking Torque ◽  
Stability Control System ◽  
Yaw Moment Control ◽  
Yaw Moment

In this paper, a vehicle stability control system is proposed to improve vehicle comfort, handling and stability. The control system includes reference model, DYC controller and Distributer. Reference model is used to obtain the desired yaw rate. DYC controller determines the desired yaw moment by means of sliding-mode technique. Distributer, based on maneuverability and comfort, distributes driving torque or braking torque according to the desired yaw rate. Simulation result shows that the proposed control algorithm can improve vehicle handling and stability effectively.

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 of in-wheel motor driven vehicle based on extension pattern recognition

2020 ◽  
Vol 103 (4) ◽  
pp. 003685042095853
Author(s):  
Wang Hongbo ◽  
Sun Youding ◽  
Tan Hongliang ◽  
Lu Yongjie
Keyword(s):  
Pattern Recognition ◽  
Reference Model ◽  
Stability Control ◽  
Joint Control ◽  
Vehicle Stability ◽  
Sideslip Angle ◽  
Distribution Method ◽  
Matlab Simulink ◽  
Yaw Rate ◽  
Control Pattern

According to the characteristics that the torque of each wheel of the in-wheel motor driven vehicle is independent and controllable, the stability control of in-wheel motor driven vehicle based on extension pattern recognition method is proposed in this paper. The dynamic model of the vehicle is established by Matlab/Simulink and Carsim. Taking two-degree-of-freedom (2-DOF) vehicle model as reference model, the vehicle yaw rate and the sideslip angle as the control objectives. The differences between the actual values and the reference values of the yaw rate and the actual sideslip angle are used to define the vehicle stability status. The vehicle stability status is divided into four stability control patterns, which are the no control pattern, the yaw rate control pattern, the yaw rate and sideslip angle joint control pattern, and the sideslip angle control pattern, respectively. The extension pattern recognition algorithm is used to determine the vehicle control pattern. The fuzzy controllers of yaw rate and sideslip angle are designed to obtain the additional yaw moment. Besides, the optimal torque distribution method is proposed by taking the lowest total energy loss of four motors as the objective function. The feasibility and effectiveness of the proposed control strategy are verified by Matlab/Simulink and Carsim joint simulation platform and hardware-in-the-loop (HIL) test.

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.

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.

A Review on Integrated Active Steering and Braking Control for Vehicle Yaw Stability System

2014 ◽  
Vol 71 (2) ◽  
Author(s):  
M.K. Aripin ◽  
Y. M. Sam ◽  
A. D. Kumeresan ◽  
M.F. Ismail ◽  
Peng Kemao
Keyword(s):  
Control System ◽  
Tracking Control ◽  
Sliding Mode ◽  
Stability Control ◽  
Yaw Rate ◽  
Active Steering ◽  
Yaw Stability ◽  
Stability Control System ◽  
Review Study ◽  
Braking Control

A review study on integrated active steering and braking control for vehicle yaw stability system is conducted and its finding is discussed in this paper. For road-vehicle dynamic, lateral dynamic control is important in order to determine the vehicle stability. The aw stability control system is the prominent approach for vehicle lateral dynamics where the actual yaw rate and sideslip should be tracked by the controller close to the desired response. To improve the performance of yaw stability control during steady state and critical driving conditions, a current approach using active control of integrated steering and braking could be implemented. This review study discusses the vehicle models, control objectives, control problems and propose control strategies for vehicle yaw stability control system. In the view of control system engineering, the transient performances of tracking control are essential. Based on the review, this paper discusses a basic concept of control strategy based on the composite nonlinear feedback (CNF) and sliding mode control (SMC) whichcan be proposed for integrated active steering and braking control in order to improve the transient performances of the yaw rate and sideslip tracking control in the presence of uncertainties and disturbances.

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.

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