A nonlinear parameter varying observer for real‐time damper force estimation of an automotive electro‐rheological suspension system

2021 ◽  
Author(s):  
Thanh‐Phong Pham ◽  
Olivier Sename ◽  
Luc Dugard
Keyword(s):  
Real Time ◽  
Suspension System ◽  
Nonlinear Parameter ◽  
Force Estimation ◽  
Parameter Varying

scholarly journals Real-time Damper Force Estimation of Vehicle Electrorheological Suspension: A NonLinear Parameter Varying Approach

2019 ◽  
Vol 52 (28) ◽  
pp. 94-99 ◽  
Author(s):  
Thanh-Phong Pham ◽  
Olivier Sename ◽  
Luc Dugard
Keyword(s):  
Real Time ◽  
Nonlinear Parameter ◽  
Force Estimation ◽  
Parameter Varying

scholarly journals Elasticity Versus Hyperelasticity Considerations in Quasistatic Modeling of a Soft Finger-Like Robotic Appendage for Real-Time Position and Force Estimation

Soft Robotics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 228-249 ◽  
Author(s):  
Ali Shiva ◽  
S.M. Hadi Sadati ◽  
Yohan Noh ◽  
Jan Fraś ◽  
Ahmad Ataka ◽  
...  
Keyword(s):  
Real Time ◽  
Force Estimation ◽  
Time Position ◽  
Soft Finger

scholarly journals Stiffness Imaging With a Continuum Appendage: Real-Time Shape and Tip Force Estimation From Base Load Readings

2020 ◽  
Vol 5 (2) ◽  
pp. 2824-2831 ◽  
Author(s):  
S.M.Hadi Sadati ◽  
Ali Shiva ◽  
Nicolas Herzig ◽  
Caleb D. Rucker ◽  
Helmut Hauser ◽  
...  
Keyword(s):  
Real Time ◽  
Force Estimation ◽  
Time Shape ◽  
Base Load

A measurement method for the overturning coefficient of high-speed trains passing through complex terrain sections under strong wind conditions

2019 ◽  
Vol 234 (8) ◽  
pp. 885-895
Author(s):  
Zhaijun Lu ◽  
Weijia Huang ◽  
Mu Zhong ◽  
Dongrun Liu ◽  
Tian Li ◽  
...  
Keyword(s):  
Real Time ◽  
Complex Terrain ◽  
High Speed ◽  
Suspension System ◽  
Strong Wind ◽  
Real Time Monitoring ◽  
Monitoring Method ◽  
High Speed Trains ◽  
Strong Winds ◽  
Pass Through

Real-time monitoring of overturning coefficients is very important for ensuring the safety of high-speed trains passing through complex terrain sections under strong wind conditions. In recent years, the phenomenon of “car swaying” that occurs when trains pass through the complex terrain has brought new challenges to ensuring the safety and riding comfort of passengers. In China, more and more high-speed trains are facing strong wind environments when running in complex terrain sections. However, due to the limitation of objective conditions, so far, only a few economical and effective methods of measurement have been developed that are suitable for real-time monitoring of the overturning coefficient of commercial vehicles. Therefore, considering the applicability and universality of such a monitoring method, this study presents a method for measuring the overturning coefficient of trains using the primary suspension system under strong winds. A vehicle test was carried out to verify the accuracy of the method. The results show that after correction, the overturning coefficient obtained from the primary suspension system is generally consistent with the overturning coefficient obtained from the instrumented wheelset. The method of measuring the overturning coefficient of trains in strong wind environments with the primary suspension system is, thus, proven feasible.

scholarly journals Gain-Scheduled Control via Convex Nonlinear Parameter Varying Models

2019 ◽  
Vol 52 (28) ◽  
pp. 70-75 ◽  
Author(s):  
Antonio Sala ◽  
Carlos Ariño ◽  
Ruben Robles
Keyword(s):  
Nonlinear Parameter ◽  
Parameter Varying ◽  
Gain Scheduled

scholarly journals Road Profile Estimation Using a 3D Sensor and Intelligent Vehicle

Sensors ◽  
2020 ◽  
Vol 20 (13) ◽  
pp. 3676 ◽  
Author(s):  
Tao Ni ◽  
Wenhang Li ◽  
Dingxuan Zhao ◽  
Zhifei Kong
Keyword(s):  
Real Time ◽  
Autonomous Vehicles ◽  
Active Suspension ◽  
Suspension System ◽  
Global Navigation Satellite Systems ◽  
Cloud Data ◽  
Uneven Terrain ◽  
Road Profile ◽  
Data Points ◽  
Profile Estimation

Autonomous vehicles can achieve accurate localization and real-time road information perception using sensors such as global navigation satellite systems (GNSSs), light detection and ranging (LiDAR), and inertial measurement units (IMUs). With road information, vehicles can navigate autonomously to a given position without traffic accidents. However, most of the research on autonomous vehicles has paid little attention to road profile information, which is a significant reference for vehicles driving on uneven terrain. Most vehicles experience violent vibrations when driving on uneven terrain, which reduce the accuracy and stability of data obtained by LiDAR and IMUs. Vehicles with an active suspension system, on the other hand, can maintain stability on uneven roads, which further guarantees sensor accuracy. In this paper, we propose a novel method for road profile estimation using LiDAR and vehicles with an active suspension system. In the former, 3D laser scanners, IMU, and GPS were used to obtain accurate pose information and real-time cloud data points, which were added to an elevation map. In the latter, the elevation map was further processed by a Kalman filter algorithm to fuse multiple cloud data points at the same cell of the map. The model predictive control (MPC) method is proposed to control the active suspension system to maintain vehicle stability, thus further reducing drifts of LiDAR and IMU data. The proposed method was carried out in outdoor environments, and the experiment results demonstrated its accuracy and effectiveness.

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