scholarly journals Deep Learning-Based Estimation of the Unknown Road Profile and State Variables for the Vehicle Suspension System

IEEE Access ◽  
2021 ◽  
Vol 9 ◽  
pp. 13878-13890
Author(s):  
Gyuwon Kim ◽  
Soo Young Lee ◽  
Jong-Seok Oh ◽  
Seungchul Lee
Keyword(s):  
Deep Learning ◽  
Suspension System ◽  
Vehicle Suspension ◽  
State Variables ◽  
Road Profile ◽  
Vehicle Suspension System

Road profile measurement using the two degrees of freedom response-type mechanism

2014 ◽  
Vol 229 (6) ◽  
pp. 1074-1087 ◽  
Author(s):  
O Kavianipour ◽  
M Montazeri-Gh ◽  
M Moazamizadeh
Keyword(s):  
Degrees Of Freedom ◽  
Suspension System ◽  
Vehicle Suspension ◽  
Profile Measurement ◽  
Response Type ◽  
Two Degrees Of Freedom ◽  
Road Profile ◽  
Measured Profile ◽  
The Time Domain ◽  
Vehicle Suspension System

This paper deals with the two degrees of freedom response-type mechanism (2 DOF RTM) designed at Iran University Science and Technology. The applications of the 2 DOF RTM are to measure the longitudinal road profile and assess the vehicle suspension system. When the 2 DOF RTM is connected to a vehicle, it is able to measure the longitudinal road profile and it is capable of assessing the vehicle suspension system while it is perched upon the exciting device. The most important part of the 2 DOF RTM is its hub planned for decreasing the vehicle movement effects on the measurement. Moreover, this paper develops a novel procedure in order to convert the measured profile from the variable speed to the constant speed. To examine the 2 DOF RTM, a profile of a road is measured by the mechanism in the time-domain, and then the highly significant roughness indices such as power spectral density (PSD) of the road unevenness, international roughness index (IRI) and present serviceability index (PSI) are estimated using the measured profile.

A study of a Kalman filter active vehicle suspension system using correlation of front and rear wheel road inputs

2000 ◽  
Vol 214 (5) ◽  
pp. 493-502 ◽  
Author(s):  
F Yu ◽  
J-W Zhang ◽  
D. A. Crolla
Keyword(s):  
Kalman Filter ◽  
Rear Wheel ◽  
Suspension System ◽  
Estimation Accuracy ◽  
Vehicle Suspension ◽  
Vehicle Speed ◽  
State Variables ◽  
Performance Improvements ◽  
Vehicle Suspension System ◽  
Active Vehicle Suspension System

Based on a half-vehicle model, an algorithm is proposed for a Kalman filter optimal active vehicle suspension system using the correlation between front and rear wheel road inputs. In this paper, two main issues were investigated, i.e. the estimation accuracy of the Kalman filter for state variables, and the potential improvements from wheelbase preview. Simulations showed good estimations from the state observer. However, if the wheelbase preview algorithm is incorporated, the estimation accuracy for the additional states significantly decreases as vehicle speed and the corresponding measurement noises increase. Significant benefits from wheelbase preview were further proved, and the available performance improvements of the rear wheel station could be up to 35 per cent. Because of the feasibility and effectiveness of the proposed algorithm, and no additional cost for measurements and sensing needs, wheelbase preview can be a promising algorithm for Kalman filter active suspension system designs.

Magnetorheological damper in semi-active vehicle suspension system using SDRE control for a quarter car model

2018 ◽  
Author(s):  
Maria Aline Gonçalves ◽  
Rodrigo Tumolin Rocha ◽  
Frederic Conrad Janzen ◽  
José Manoel Balthazar ◽  
Angelo Marcelo Tusset
Keyword(s):  
Suspension System ◽  
Magnetorheological Damper ◽  
Vehicle Suspension ◽  
Quarter Car Model ◽  
Car Model ◽  
Quarter Car ◽  
Vehicle Suspension System ◽  
Active Vehicle Suspension System

A Pneumatic Artificial Muscle Bionic Kangaroo Leg Suspension

2019 ◽  
Vol 12 (4) ◽  
pp. 357-366
Author(s):  
Yong Song ◽  
Shichuang Liu ◽  
Jiangxuan Che ◽  
Jinyi Lian ◽  
Zhanlong Li ◽  
...  
Keyword(s):  
Strong Shock ◽  
Artificial Muscle ◽  
Suspension System ◽  
Vehicle Body ◽  
Vehicle Suspension ◽  
Pneumatic Artificial Muscle ◽  
Advantages And Disadvantages ◽  
Road Excitation ◽  
Vehicle Suspension System ◽  
Suspension Structure

Background: Vehicles generally travel on different road conditions, and withstand strong shock and vibration. In order to reduce or isolate the strong shock and vibration, it is necessary to propose and develop a high-performance vehicle suspension system. Objective: This study aims to report a pneumatic artificial muscle bionic kangaroo leg suspension to improve the comfort performance of vehicle suspension system. Methods: In summarizing the existing vehicle suspension systems and analyzing their advantages and disadvantages, this paper introduces a new patent of vehicle suspension system based on the excellent damping and buffering performance of kangaroo leg, A Pneumatic Artificial Muscle Bionic Kangaroo Leg Suspension. According to the biomimetic principle, the pneumatic artificial muscles bionic kangaroo leg suspension with equal bone ratio is constructed on the basis of the kangaroo leg crural index, and two working modes (passive and active modes) are designed for the suspension. Moreover, the working principle of the suspension system is introduced, and the rod system equations for the suspension structure are built up. The characteristic simulation model of this bionic suspension is established in Adams, and the vertical performance is analysed. Results: It is found that the largest deformation happens in the bionic heel spring and the largest angle change occurs in the bionic ankle joint under impulse road excitation, which is similar to the dynamic characteristics of kangaroo leg. Furthermore, the dynamic displacement and the acceleration of the vehicle body are both sharply reduced. Conclusion: The simulation results show that the comfort performance of this bionic suspension is excellent under the impulse road excitation, which indicates the bionic suspension structure is feasible and reasonable to be applied to vehicle suspensions.

scholarly journals Modelling and simulation of motor vehicle suspension system

2021 ◽  
Vol 1107 (1) ◽  
pp. 012092
Author(s):  
Eyere Emagbetere ◽  
Peter A. Oghenekovwo ◽  
Christabel C. Obinabo ◽  
Abraham K. Aworinde ◽  
Felix A. Ishola ◽  
...  
Keyword(s):  
Motor Vehicle ◽  
Suspension System ◽  
Modelling And Simulation ◽  
Vehicle Suspension ◽  
Vehicle Suspension System
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