Stability Optimal Control Based on 15-Dof Vehicle Model

2011 ◽  
Vol 299-300 ◽  
pp. 1266-1270 ◽  
Author(s):  
Xiao Bin Fan ◽  
Yu Jiang ◽  
Hui Gang Wang
Keyword(s):  
Control System ◽  
Estimation Algorithm ◽  
Stability Control ◽  
Slip Angle ◽  
Vehicle Model ◽  
Simulation Test ◽  
Side Slip Angle ◽  
Handling Characteristics ◽  
Stability Control System ◽  
The Stability

The 15 degrees of nonlinear dynamic vehicle model is established, and then Dugoff and magic formula tire model are studied by comparison. Then the braking system dynamics model and the side-slip angle estimation algorithm are discussed. Stability control system is established based on the control target of combined yaw and side-slip angle with linear 2-dof vehicle handling characteristics model. It shows that control system can perfectly control vehicle driving and can improve the stability of car active safety through the serpent’s simulation test.

Design and Simulation of a Stability Control System for 4 Independent Wheel Drive Electric Vehicle

2016 ◽  
Author(s):  
Juan S. Núñez ◽  
Luis E. Muñoz
Keyword(s):  
Control System ◽  
Electric Vehicle ◽  
Sliding Mode ◽  
Stability Control ◽  
Performance Comparison ◽  
Phase Portraits ◽  
Vehicle Model ◽  
Stability Control System ◽  
The Stability ◽  
Yaw Moment

With the aim of prevent situations of vehicle instability against different driving maneuvers, the vehicle yaw stability becomes crucial for safe operation. This paper presents the design and simulation of a traction and a stability control system algorithms for independent four-wheel-driven electric vehicle. The stability control system consists of a multilevel algorithm divided into a high level controller and a low level controller. First, an analysis of the stability of the vehicle is performed using phase portraits analysis, both in open loop and closed loop. The stability control system is designed to generate a desired yaw moment according to the steady state cornering relationship with the steering angle input. As the test vehicle, a 14 DoF vehicle model is proposed including nonlinear tire models that allow the generation of combined forces. The vehicle model includes the powertrain dynamics. The yaw moment generation is performed using the traction and braking forces between the tires of each side of both front and rear axle. In order to generate the maximum traction forces in each of the wheels, a traction and a braking control is developed via a sliding mode controller scheme. Finally a performance comparison between a controlled and an uncontrolled vehicle is presented. The behavior of both vehicles is simulated using a classical double lane change driving maneuver.

Design and Implementation of Large-Scale Temperature Stability Control System

2014 ◽  
Vol 644-650 ◽  
pp. 313-316
Author(s):  
Wen Lai Liu
Keyword(s):  
Control System ◽  
System Design ◽  
Large Scale ◽  
Control Method ◽  
Temperature Stability ◽  
Stability Control ◽  
Control System Design ◽  
Scale Temperature ◽  
Stability Control System ◽  
The Stability

large-scale temperature stability control method is studied in this paper. In the process of large-scale temperature control, the stability of control is a very important indicator. To this end, this paper proposes a large-scale temperature stability control algorithm based on hierarchical control method. Balance equation of large-scale temperature stability control is created for the effective transmission of control data. According to the constant control theory, large-scale temperature stability control system design is achieved. Experimental results show that the proposed algorithm for large-scale temperature stability control system design, can greatly improve the stability of control, and get the satisfactory results.

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.

A comparison on optimal torque vectoring strategies in overall performance enhancement of a passenger car

2016 ◽  
Vol 230 (4) ◽  
pp. 469-488 ◽  
Author(s):  
Seyed Mohammad Mehdi Jaafari ◽  
Kourosh Heidari Shirazi
Keyword(s):  
Fuel Consumption ◽  
Ride Comfort ◽  
Stability Control ◽  
Electronic Stability Control ◽  
Slip Angle ◽  
Vehicle Model ◽  
Side Slip Angle ◽  
Wheel Drive ◽  
Torque Vectoring ◽  
Four Wheel Drive

In this paper, a comparison is made on different torque vectoring strategies to find the best strategy in terms of improving handling, fuel consumption, stability and ride comfort performances. The torque vectoring differential strategies include superposition clutch, stationary clutch, four-wheel drive and electronic stability control. The torque vectoring differentials are implemented on an eight-DOF vehicle model and controlled using optimized fuzzy-based controllers. The vehicle model assisted with the Pacejka tyre model, an eight-cylinder dynamic model for engine, and a five-speed transmission system. Bee’s Algorithm is employed to optimize the fuzzy controller to ensure each torque vectoring differential works in its best state. The controller actuates the electronic clutches of the torque vectoring differential to minimize the yaw rate error and limiting the side-slip angle in stability region. To estimate side-slip angle and cornering stiffness, a combined observer is designed based on full order observer and recursive least square method. To validate the results, a realistic car model is built in Carsim package. The final model is tested using a co-simulation between Matlab and Carsim. According to the results, the torque vectoring differential shows better handling compared to electronic stability control, while electronic stability control is more effective in improving the stability in critical situation. Among the torque vectoring differential strategies, stationary clutch in handling and four-wheel drive in fuel consumption as well as ride comfort have better operation and more enhancements.

Vehicle side-slip angle estimation on a banked and low-friction road

2017 ◽  
Vol 232 (12) ◽  
pp. 1584-1596 ◽  
Author(s):  
Martin Haudum ◽  
Johannes Edelmann ◽  
Manfred Plöchl ◽  
Manuel Höll
Keyword(s):  
Vehicle Dynamics ◽  
Stability Control ◽  
Velocity Estimation ◽  
Slip Angle ◽  
State Variables ◽  
Low Friction ◽  
Bank Angle ◽  
Angle Estimation ◽  
Side Slip Angle ◽  
Handling Characteristics

The effective application of integrated vehicle dynamics control and automatic driving require consistent vehicle state variables and parameters. Considering lateral vehicle dynamics, the yaw rate and (estimated) vehicle side-slip angle are the minimum set of state variables that can give insight into the handling characteristics of a vehicle. Various methods of vehicle side-slip angle (lateral velocity) estimation have been tested in virtual and real world applications, in particular on a horizontal dry road. Vehicle side-slip angle, however, is not only affected by the (steering) commands of the driver, and possibly by a vehicle dynamics controller, but can also arise from a banked road or result from a low-friction surface, changing tyre–road contact. The combined effects require a comprehensive estimation approach, which is less often touched upon in the literature. Based on earlier findings on important aspects of observability, the paper addresses a modular vehicle side-slip angle estimation approach that is particularly focused upon practical aspects of modelling and design. Estimation of the combined vehicle side-slip angle, road bank angle and maximum tyre–road friction coefficient has been broadly tested with a vehicle equipped with an electronic stability control (ESC) and electric power-assisted steering (EPS) sensor configuration, for various road conditions, driving situations and vehicle/tyre setups.

A Haptic System to Enhance Stability of Heavy Duty Machines

2008 ◽  
Author(s):  
Giulio Rosati ◽  
Andrea Biondi ◽  
Stefano Cenci ◽  
Aldo Rossi ◽  
Giovanni Boschetti
Keyword(s):  
Control System ◽  
Degrees Of Freedom ◽  
Force Feedback ◽  
Stability Control ◽  
Working Environment ◽  
Heavy Duty ◽  
Stability Control System ◽  
In Series ◽  
The Stability ◽  
Machine Interaction

The operator’s safety is a very crucial issue in heavy duty machines. The dangerousness of the working environment forces the operator to consider several parameters at the same time, and a large amount of practice and concentration are usually required to operate the machine safely. In fact, man-machine interaction is often very poor in these kind of machines. With the aim of improving this interaction and enhancing stability, a stability control system is proposed, which is based on a simplified model of the machine and on a 2-degrees-of-freedom force-feedback joystick, and combines the use of a passive ad an active actuator. Active drives can generate high-fidelity sensations but, in general, they have a poor torque/size ratio, they are expensive and can become dangerous for the operator. On the other hand, passive devices (like brakes) can generate higher torques, are cheaper and safer, however they cannot provide energy to the system but only dissipate it; this feature reduces the range of sensations that can be rendered to the operator. In order to partially overcome this limitation, an elastic element is integrated in series with the brake. In this way, it is possible to store some potential energy that can be returned to the operator to give a better sensation of the proximity of an instability working condition for the machine. In the paper, the stability control system is introduced, and the design of the haptic joystick is presented. First experimental results on a simulator are finally illustrated.

Development of Trunk Transmission Online Integrated Stability Control System (ISC) for New Generation Grid

2017 ◽  
Vol 137 (6) ◽  
pp. 434-445 ◽  
Author(s):  
Hiroshi Yoshida ◽  
Ryuji Tachi ◽  
Koya Takafuji ◽  
Hironori Imaeda ◽  
Masaru Takeishi ◽  
...  
Keyword(s):  
Control System ◽  
Stability Control ◽  
Stability Control System ◽  
New Generation

Development of On-line Integrated Stability Control System (ISC) for Long-distance Bulk Power Transmission

2013 ◽  
Vol 133 (4) ◽  
pp. 313-323 ◽  
Author(s):  
Kuniaki Anzai ◽  
Kimihiko Shimomura ◽  
Soshi Yoshiyama ◽  
Hiroyuki Taguchi ◽  
Masaru Takeishi ◽  
...  
Keyword(s):  
Control System ◽  
Power Transmission ◽  
Stability Control ◽  
Long Distance ◽  
Stability Control System ◽  
On Line ◽  
Bulk Power
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