Roll Stability Indicator Incorporating Roll Center Movement

2007 ◽  
Author(s):  
Jongchul Jung ◽  
Taehyun Shim ◽  
Jamie Gertsch
Keyword(s):  
Parameter Identification ◽  
Threshold Value ◽  
Vertical Movement ◽  
Roll Angle ◽  
Roll Motion ◽  
Identification Algorithm ◽  
Simple Task ◽  
Rollover Prevention ◽  
Vehicle Rollover ◽  
Robust Parameter

Predicting impending vehicle rollover is essential for rollover prevention systems but it is not a simple task. In describing roll motion, the roll center movement becomes important as the vehicle roll angle increases, and thus affects the performance of rollover warning devices. This paper proposes a dynamic roll stability indicator incorporating roll center movement. A robust parameter identification algorithm is designed to estimate the horizontal and vertical movement of the roll center. This estimate is used in the roll stability indicator to update its rollover threshold value. The effectiveness of the proposed roll stability indicator is demonstrated through simulations.

A Novel Active Rollover Prevention for Ground Vehicles Based on Continuous Roll Motion Detection

2018 ◽  
Vol 141 (1) ◽  
Author(s):  
Fengchen Wang ◽  
Yan Chen
Keyword(s):  
Motion Detection ◽  
Hierarchical Control ◽  
Roll Motion ◽  
Ground Vehicles ◽  
Rollover Prevention ◽  
Hierarchical Control Architecture ◽  
Center Position ◽  
Vehicle Rollover ◽  
Lift Off ◽  
The Given

To enhance the performance of vehicle rollover detection and prevention, this paper proposes a novel control strategy integrating the mass-center-position (MCP) metric and the active rollover preventer (ARPer) system. The applied MCP metric can provide completed rollover information without saturation in the case of tire lift-off. Based on the continuous roll motion detection provided by the MCP metric, the proposed ARPer system can generate corrective control efforts independent to tire–road interactions. Moreover, the capability of the ARPer system is investigated for the given vehicle physical spatial constraints. A hierarchical control architecture is also designed for tracking desired accelerations derived from the MCP metric and allocating control efforts to the ARPer system and the active front steering (AFS) control. Cosimulations between CarSim® and MATLAB/SIMULINK with a fishhook maneuver are conducted to verify the control performance. The results show that the vehicle with the assistance of the ARPer system can successfully achieve better performance of vehicle rollover prevention, compared with an uncontrolled vehicle and an AFS-controlled vehicle.

Vehicle Rollover Detection Index

2009 ◽  
Author(s):  
Nikolai Moshchuk ◽  
Shih-Ken Chen
Keyword(s):  
Roll Angle ◽  
Lateral Acceleration ◽  
Control Action ◽  
Roll Motion ◽  
Steering Control ◽  
Velocity Sensor ◽  
Quality Of Control ◽  
Displacement Sensors ◽  
Vehicle Rollover ◽  
Roll Velocity

Vehicle rollover resistance can be enhanced using differential braking control, rear-wheel steering, front steering control, suspension damping control, or the combination of any of them. In each control action, the controller receives the vehicle dynamics information from various sensors, such as a yaw-velocity sensor, a lateral accelerometer, a roll-velocity sensor (if available), and suspension displacement sensors (if available), and determines a control action to be taken. A balance between controlling vehicle roll motion and vehicle yaw motion needs to be reached to achieve the optimal vehicle response. Therefore, detection of the current and impending vehicle situations, especially pertaining to the vehicle roll, is vital for the quality of control. Various methodologies have been developed in the past to detect vehicle situations that may lead to rollover. Generally, rollover detection is based on roll velocity sensor and roll angle estimation. In most of these methods, thresholds for roll velocity and roll angle are established to detect imminent rollover. Even though roll velocity and roll angle are the two most important elements in vehicle roll motion, they may not be sufficient to timely detect impending rollover. This paper proposes a vehicle rollover detection index that can be used in a control algorithm to enhance vehicle rollover resistance. It uses roll angle and velocity (measured or estimated), yaw velocity, vehicle speed, and lateral acceleration. Further enhancement to the proposed algorithm is then briefly discussed that includes the effect of vehicle lateral load transfer on the detection.

An improved nonlinear innovation-based parameter identification algorithm for ship models

2021 ◽  
pp. 1-9
Author(s):  
Baigang Zhao ◽  
Xianku Zhang
Keyword(s):  
Parameter Identification ◽  
Test Data ◽  
Stochastic Gradient ◽  
Least Square ◽  
Gradient Algorithm ◽  
Model Parameters ◽  
Identification Algorithm ◽  
Process Innovations ◽  
Stochastic Gradient Algorithm ◽  
Tangent Function

Abstract To solve the problem of identifying ship model parameters quickly and accurately with the least test data, this paper proposes a nonlinear innovation parameter identification algorithm for ship models. This is based on a nonlinear arc tangent function that can process innovations on the basis of an original stochastic gradient algorithm. A simulation was carried out on the ship Yu Peng using 26 sets of test data to compare the parameter identification capability of a least square algorithm, the original stochastic gradient algorithm and the improved stochastic gradient algorithm. The results indicate that the improved algorithm enhances the accuracy of the parameter identification by about 12% when compared with the least squares algorithm. The effectiveness of the algorithm was further verified by a simulation of the ship Yu Kun. The results confirm the algorithm's capacity to rapidly produce highly accurate parameter identification on the basis of relatively small datasets. The approach can be extended to other parameter identification systems where only a small amount of test data is available.

scholarly journals Physical Parameter Identification Algorithm Modeling for Complex Microsystem via Triangulation Functions

2018 ◽  
Vol 7 (4.36) ◽  
pp. 962
Author(s):  
Elena N. Meshcheryakova ◽  
. .
Keyword(s):  
Parameter Identification ◽  
Physical Parameter ◽  
Delaunay Triangulation ◽  
Model Development ◽  
Physical Object ◽  
Identification Algorithm ◽  
Classification Analysis ◽  
Advantages And Disadvantages ◽  
Object Parameters ◽  
D Model

This article describes the possibility of triangulation function use for the classification, analysis and identification of complex microsystem physical object parameters. They analyzed the existing methods and identification algorithms, their advantages and disadvantages are highlighted. The existing methods of triangulation are considered, the possibility of Delaunay triangulation is described for surfactant signal 3-D model development and analysis. They developed the algorithm to identify the state of an object using the triangulation function that takes into account the change of node coordinates and the length of the triangulation grid edges. They presented the visual UML model. The conclusions are drawn about the possibility of triangulation function use for the analysis of complex microsystem state.  

Detection of Vehicle Tripped and Untripped Rollovers by a Novel Index With Mass-Center-Position Estimations

2017 ◽  
Author(s):  
Fengchen Wang ◽  
Yan Chen
Keyword(s):  
Load Transfer ◽  
Center Of Mass ◽  
Roll Motion ◽  
Mass Center ◽  
Motion Models ◽  
Center Position ◽  
Before And After ◽  
Vehicle Rollover ◽  
Lift Off ◽  
Rollover Index

This paper presents a novel mass-center-position (MCP) metric for vehicle rollover propensity detection. MCP is first determined by estimating the positions of the center of mass of one sprung mass and two unsprung masses with two switchable roll motion models, before and after tire lift-off. The roll motion information without saturation can then be provided through MCP continuously. Moreover, to detect completed rollover statues for both tripped and untripped rollovers, the criteria are derived from d’Alembert principle and moment balance conditions based on MCP. In addition to tire lift-off, three new rollover statues, rollover threshold, rollover occurrence, and vehicle jumping into air can be all identified by the proposed criteria. Compared with an existing rollover index, lateral load transfer ratio, the fishhook maneuver simulation results in CarSim® for an E-class SUV show that MCP metric can successfully predict the vehicle impending rollover without saturation for untripped rollovers. Tripped rollovers caused by a triangle road bump are also successfully detected in the simulation. Thus, MCP metric can be successfully applied for rollover propensity prediction.

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