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.

Study on Fuzzy Control for Vehicle Dynamic Stability

2012 ◽  
Vol 430-432 ◽  
pp. 1747-1750
Author(s):  
Feng Du ◽  
Zhi Wei Guan ◽  
Guang Hui Yan
Keyword(s):  
Fuzzy Control ◽  
Control Strategy ◽  
Fuzzy Controller ◽  
Rear Wheel ◽  
Stability Control ◽  
Vehicle Stability ◽  
Vehicle Dynamic ◽  
Control Effect ◽  
Vehicle Stability Control ◽  
Driving Stability

To ensure vehicle stability in critical and dangerous working conditions, a vehicle stability control strategy is proposed, which is to generate compensating yaw moment by using the combined action of active rear-wheel steering and differential braking. A corresponding fuzzy controller for the proposed control strategy is designed. To verify the control effect of fuzzy controller, the numerical simulation by using vehicle dynamic model is performed in critical condition. The simulation results show that the designed fuzzy control system can efficiently prevent the vehicle to lose driving stability during critical turning.

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.

Design of Direct Yaw Moment Control System to Enhance Vehicle Stability Based on Fuzzy Logic

2011 ◽  
Vol 383-390 ◽  
pp. 1326-1332 ◽  
Author(s):  
Zhe Xu ◽  
Min Xiang Wei ◽  
Yang Wang ◽  
Jian Wei Wei
Keyword(s):  
Control System ◽  
High Speed ◽  
Control Method ◽  
Reference Value ◽  
Stability Control ◽  
Slip Angle ◽  
Vehicle Stability ◽  
Handling Performance ◽  
Model Following Control ◽  
Yaw Moment

Vehicle running at high speed if affected by crosswind or steering handling may spin or drift out since the yaw moment produced is not big enough to stabilize it. In order to prevent these dangerous situations, a fuzzy direct yaw moment controller is designed in this paper, since it is simple and suitable for nonlinear system. This vehicle stability control system is based on model following control method. The side slip angle and yaw rate which indicate the vehicle’s stability and handling performance are chosen as the control variables. The response of the bicycle model is selected as the reference value. In order to evaluate the performance of the controller, simulations of lane change and J-turn maneuver are carried out. The results show that the stability and handling performance of the vehicle are improved.

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.

Hybrid-type stability control method based on combination of conventional PSS and fuzzy control

1994 ◽  
Vol 114 (6) ◽  
pp. 76-89
Author(s):  
Marzan Aziz Iskandar ◽  
Masashi Satoh ◽  
Yoshibumi Mizutani ◽  
Akio Suzuki ◽  
Mitsuo Ishizeki
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Stability Control ◽  
Hybrid Type

scholarly journals Simulation analysis of efficiency and stability for vehicle’s ABS control on combined steering and braking maneuvers

2019 ◽  
Vol 272 ◽  
pp. 01024 ◽  
Author(s):  
Feng YU ◽  
Jun XIE
Keyword(s):  
High Speed ◽  
Control Algorithm ◽  
Degrees Of Freedom ◽  
Simulation Analysis ◽  
Stability Control ◽  
Vehicle Stability ◽  
Slip Ratio ◽  
Braking Performance ◽  
Braking System ◽  
The Stability

Eight degrees of freedom vehicle model was established. Using the method of fuzzy control, the ABS control algorithm was designed based on slip ratio. Simulation analysis was done at speed of 15m/s, 20m/s, 25m/s under turning braking. The results show that the vehicle braking performance and vehicle stability at middle or low speed was improved by using the ABS controller, but qualitative analysis shows that phenomenon of vehicle instability was appeared at high-speed conditions. The turning braking stability under ABS controller was judged quantificationally by the stability judging formula. The results show that the requirements of stability control could not meet with only Anti-lock Braking System.

scholarly journals Fuzzy Control of Cold Storage Refrigeration System with Dynamic Coupling Compensation

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Xiliang Ma ◽  
Ruiqing Mao
Keyword(s):  
Adverse Effects ◽  
Fuzzy Control ◽  
Cold Storage ◽  
Control Strategy ◽  
Control Method ◽  
Fuzzy Controller ◽  
Variable Load ◽  
Variable Degree ◽  
Refrigeration System ◽  
Dynamic Coupling

Cold storage refrigeration systems possess the characteristics of multiple input and output and strong coupling, which brings challenges to the optimize control. To reduce the adverse effects of the coupling and improve the overall control performance of cold storage refrigeration systems, a control strategy with dynamic coupling compensation was studied. First, dynamic model of a cold storage refrigeration system was established based on the requirements of the control system. At the same time, the coupling between the components was studied. Second, to reduce the adverse effects of the coupling, a fuzzy controller with dynamic coupling compensation was designed. As for the fuzzy controller, a self-tuning fuzzy controller was served as the primary controller, and an adaptive neural network was adopted to compensate the dynamic coupling. Finally, the proposed control strategy was employed to the cold storage refrigeration system, and simulations were carried out in the condition of start-up, variable load, and variable degree of superheat, respectively. The simulation results verify the effectiveness of the fuzzy control method with dynamic coupling compensation.

scholarly journals Vehicle Stability Control Strategy Based on Recognition of Driver Turning Intention for Dual-Motor Drive Electric Vehicle

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Shu Wang ◽  
Xuan Zhao ◽  
Qiang Yu
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Control Strategy ◽  
Stability Control ◽  
Motor Drive ◽  
Vehicle Stability ◽  
Lane Change ◽  
Vehicle Stability Control ◽  
Stability Control System ◽  
The Stability

Vehicle stability control should accurately interpret the driving intention and ensure that the actual state of the vehicle is as consistent as possible with the desired state. This paper proposes a vehicle stability control strategy, which is based on recognition of the driver’s turning intention, for a dual-motor drive electric vehicle. A hybrid model consisting of Gaussian mixture hidden Markov (GHMM) and Generalized Growing and Pruning RBF (GGAP-RBF) neural network is constructed to recognize the driver turning intention in real time. The turning urgency coefficient, which is computed on the basis of the recognition results, is used to establish a modified reference model for vehicle stability control. Then, the upper controller of the vehicle stability control system is constructed using the linear model predictive control theory. The minimum of the quadratic sum of the working load rate of the vehicle tire is taken as the optimization objective. The tire-road adhesion condition, performance of the motor and braking system, and state of the motor are taken as constraints. In addition, a lower controller is established for the vehicle stability control system, with the task of optimizing the allocation of additional yaw moment. Finally, vehicle tests were carried out by conducting double-lane change and single-lane change experiments on a platform for dual-motor drive electric vehicles by using the virtual controller of the A&D5435 hardware. The results show that the stability control system functions appropriately using this control strategy and effectively improves the stability of the vehicle.

Estimation of Vehicle Sideslip Angle Using Strong Tracking SRUKF

2014 ◽  
Vol 614 ◽  
pp. 267-270
Author(s):  
Jian Feng Chen ◽  
Xiao Dong Sun ◽  
Long Chen ◽  
Hao Bin Jiang
Keyword(s):  
Numerical Simulations ◽  
High Speed ◽  
State Observer ◽  
Stability Control ◽  
Vehicle Model ◽  
Sideslip Angle ◽  
Magic Formula ◽  
The Stability ◽  
Accuracy Performance ◽  
Better Than

Sideslip angle is an important parameter for the stability control of high-speed vehicles. In this paper, a novel state observer based on strong tracking SRUKF is presented to estimate the sideslip angle. Besides the strong tracking SRUKF algorithm, a 2-DOF vehicle model and a “Magic Formula” are utilized to construct the state observer. Numerical simulations are implemented to testify on the accuracy performance of estimation based on the strong tracking SRUKF and standard UKF. The results show that the trends using two types of filters are accordant with the theoretic values, and the accuracy of the former is better than the latter.

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