scholarly journals Active Suspension Control Strategy of Multi-Axle Emergency Rescue Vehicle Based on Inertial Measurement Unit

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6877
Author(s):  
Qinghe Guo ◽  
Dingxuan Zhao ◽  
Xiaolong Zhao ◽  
Zhenxing Li ◽  
Xiaobo Shi
Keyword(s):  
Control Strategy ◽  
Parallel Mechanism ◽  
Inertial Measurement Unit ◽  
Ride Comfort ◽  
Active Suspension ◽  
Vehicle Body ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Suspension Control ◽  
Pneumatic Suspension

Active suspension control strategies are a top priority in active suspension system. The current research on active suspension control strategies is mostly focused on two-axle vehicles, and there is less research investigating multi-axle vehicles. Additionally, their effective implementation is dependent on accurate mathematical models, and most of them adopt force feedback control, which is vulnerable to external interference. To solve these problems, this paper proposes an active suspension control strategy based on Inertial Measurement Unit. The multi-axle emergency rescue vehicle is made to be equivalent to a 3-degrees-of-freedom parallel mechanism by using the method of grouping and interconnecting the suspension units of the whole vehicle. The attitude change of the vehicle body was transformed into the servo actuator’s displacement by solving the inverse solution of the parallel mechanism position and the action of the servo actuator was driven in reverse according to the displacement obtained. In this way, the vehicle body attitude can be compensated, and the ride comfort and the handling stability of the vehicle can be improved. To verify the effectiveness of the control strategy proposed, the three-axle six vehicle was taken as the research object, the position inverse solution of its equivalent 3-degrees-of-freedom parallel mechanism was deduced, and a high-pass filter was designed. The three-axle vehicle experiment platform integrating active suspension and hydro-pneumatic suspension was built, and the gravel road and slope road experiments were carried out and the results compared with those obtained with hydro-pneumatic suspension. The experiment results showed that, compared with hydro-pneumatic suspension, the active suspension control strategy based on Inertial Measurement Unit proposed in this paper can not only stabilize the body attitude, but also effectively suppress body vibration, improving the ride comfort and handling stability of the vehicle significantly.

System Identification and Optimal Control of Half-Car Active Suspension System Using a Single Noisy IMU With Position Uncertainty

2017 ◽  
Author(s):  
Tamer Attia ◽  
Kevin Kochersberger ◽  
John Bird ◽  
Steve C. Southward
Keyword(s):  
System Identification ◽  
Channel Model ◽  
Ride Comfort ◽  
Active Suspension ◽  
Measurement Unit ◽  
Linear Quadratic ◽  
Position Uncertainty ◽  
Inertial Measurement ◽  
Handling Characteristics ◽  
Frequency Domain Methods

An active suspension based on Linear Quadratic Gaussian (LQG) optimal controller is an effective system for enhancing the ride comfort and handling characteristics of a vehicle. LQG requires a good plant model for success, and this may be difficult to extract using a single inertial measurement device in the presence of noise. This paper presents a method for estimating the vehicle states by measuring both the vehicle bounce and pitch accelerations using an Inertial Measurement Unit (IMU) with position uncertainty relative to the sprung mass center of gravity. Frequency domain methods are used for System Identification (SysId). The state estimation is based on channel-by-channel model estimation using uncorrelated random excitation which is applied to the front wheels, rear wheels, front actuator, and rear actuator. An anti-aliasing filter eliminates false response harmonics and a Kalman filter is used to estimate the current states of the actual plant and the LQR block for the full-states-feedback controller. The controllers and observer are implemented in simulation using a four degree-of-freedom half car linear model.

The Implementation of Skyhook Control for Semi-Active Suspension Based on Vi-CarRealTime

2015 ◽  
Vol 713-715 ◽  
pp. 748-751 ◽  
Author(s):  
Bo Wei Bi ◽  
Fang Xiao
Keyword(s):  
Control Strategy ◽  
Ride Comfort ◽  
Active Suspension ◽  
Vehicle Model ◽  
Control Range ◽  
Control Signals ◽  
Suspension Control ◽  
Automobile Suspension ◽  
Simulation Results ◽  
Damper Control

The research of semi active suspension control strategy once was a hot point in the field of automobile suspension [2, 3], but it is difficult to achieve for most of them. I choose VI-CarRealTime to build vehicle model based on ADAMS vehicle model. Kalman Filter designed based on 1/2 vehicle model supply control signals for controller. Considering characteristics of CDC damper, Skyhook control strategy is applied for simulation, the simulation results show that, Skyhook Control can improve vehicle ride comfort in CDC damper control range.

Based on Single Neuron PID Control of Vehicle Active Suspension System

2013 ◽  
Vol 380-384 ◽  
pp. 528-531 ◽  
Author(s):  
Xiao Feng Liu ◽  
Xin Hua Xie
Keyword(s):  
Control Strategy ◽  
Pid Control ◽  
Single Neuron ◽  
Ride Comfort ◽  
Control Strategies ◽  
Active Suspension ◽  
Vital Role ◽  
Suspension System ◽  
Suspension Control ◽  
Single Neuron Pid

Relative to the passive suspension, automotive active suspension car driving more ride comfort and stability, has a vital role to further improve the performance of the vehicle. For such a typically complex active suspension system research, the key issue is the selection of control strategies. The problems in the currently active suspension control strategy, the principle of a simple, effective, this paper, a single neuron PID control strategy used in the automotive active suspension system. The results show that compared with other control strategies, single neuron PID control strategy is reliable, has more advantages.

Body attitude control strategy based on road level for heavy rescue vehicles

2020 ◽  
pp. 095440702096616
Author(s):  
Hao Chen ◽  
Mingde Gong ◽  
Dingxuan Zhao ◽  
Jianxu Zhu
Keyword(s):  
Control Strategy ◽  
Attitude Control ◽  
Ride Comfort ◽  
Active Suspension ◽  
Suspension System ◽  
Vehicle Body ◽  
Vertical Acceleration ◽  
Inference System ◽  
The Road ◽  
Body Attitude

This paper proposes an attitude control strategy based on road level for heavy rescue vehicles. The strategy aims to address the problem of poor ride comfort and stability of heavy rescue vehicles in complex road conditions. Firstly, with the pressure of the suspension hydraulic cylinder chamber without a piston rod as the parameter, Takagi–Sugeno fuzzy controller classification and adaptive network-based fuzzy inference system controller classification are used to recognise the road level. Secondly, particle swarm optimisation is adopted to obtain the optimal parameters of the active suspension system of vehicle body attitude control under different road levels. Lastly, the parameters of the active suspension system are selected in accordance with the road level recognised in the driving process to improve the adaptive adjustment capability of the active suspension system at different road levels. Test results show that the root mean square values of vertical acceleration, pitch angle and roll angle of the vehicle body are reduced by 59.9%, 76.2% and 68.4%, respectively. This reduction improves the ride comfort and stability of heavy rescue vehicles in complex road conditions.

Wheelbase preview control of an active suspension with a disturbance-decoupled observer to improve vehicle ride comfort

2019 ◽  
Vol 234 (6) ◽  
pp. 1725-1745
Author(s):  
Baek-soon Kwon ◽  
Daejun Kang ◽  
Kyongsu Yi
Keyword(s):  
Simulation Study ◽  
Control Algorithm ◽  
Ride Comfort ◽  
Active Suspension ◽  
The Body ◽  
Vehicle Body ◽  
Preview Control ◽  
The Road ◽  
Road Disturbance ◽  
Suspension Control

This article deals with the design of a partial preview active suspension control algorithm for the improvement of vehicle ride comfort. Generally, while preview-controlled active suspension systems have even greater potential than feedback-controlled systems, their main challenge is obtaining preview information of the road profile ahead. A critical drawback of the “look-ahead” sensors is an increased risk of incorrect detection influenced by water, snow, and other soft obstacles on the road. In this work, a feasible wheelbase preview suspension control algorithm without information about the road elevation has been developed based on a novel 3-degree-of-freedom full-car dynamic model which incorporates only the vehicle body dynamics. The main advantage of the employed vehicle model is that the system disturbance input vector consists of vertical wheel accelerations that can be measured easily. The measured acceleration information of the front wheels is used for predictive control of the rear suspension to stabilize the body motion. The suspension state estimator has also been designed to completely remove the effect of unknown road disturbance on the state estimation error. The estimation performance of an observer is verified via a simulation study and field tests. The performance of the proposed suspension controller is evaluated on a frequency domain and time domain via a simulation study. It is shown that the vehicle ride comfort can be improved more by the proposed wheelbase preview control approach than by the feedback approach.

scholarly journals Automatically evaluating balance using machine learning and data from a single inertial measurement unit

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fahad Kamran ◽  
Kathryn Harrold ◽  
Jonathan Zwier ◽  
Wendy Carender ◽  
Tian Bao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inertial Measurement Unit ◽  
Physical Therapists ◽  
Physical Therapist ◽  
Model Performance ◽  
Machine Learning Techniques ◽  
Measurement Unit ◽  
Balance Performance ◽  
Inertial Measurement ◽  
Self Assessments

Abstract Background Recently, machine learning techniques have been applied to data collected from inertial measurement units to automatically assess balance, but rely on hand-engineered features. We explore the utility of machine learning to automatically extract important features from inertial measurement unit data for balance assessment. Findings Ten participants with balance concerns performed multiple balance exercises in a laboratory setting while wearing an inertial measurement unit on their lower back. Physical therapists watched video recordings of participants performing the exercises and rated balance on a 5-point scale. We trained machine learning models using different representations of the unprocessed inertial measurement unit data to estimate physical therapist ratings. On a held-out test set, we compared these learned models to one another, to participants’ self-assessments of balance, and to models trained using hand-engineered features. Utilizing the unprocessed kinematic data from the inertial measurement unit provided significant improvements over both self-assessments and models using hand-engineered features (AUROC of 0.806 vs. 0.768, 0.665). Conclusions Unprocessed data from an inertial measurement unit used as input to a machine learning model produced accurate estimates of balance performance. The ability to learn from unprocessed data presents a potentially generalizable approach for assessing balance without the need for labor-intensive feature engineering, while maintaining comparable model performance.

scholarly journals Inertial Measurement Unit Sensors in Assistive Technologies for Visually Impaired People, a Review

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4767
Author(s):  
Karla Miriam Reyes Leiva ◽  
Milagros Jaén-Vargas ◽  
Benito Codina ◽  
José Javier Serrano Olmedo
Keyword(s):  
Inertial Measurement Unit ◽  
Visually Impaired ◽  
Inertial Sensors ◽  
Assistive Technologies ◽  
Biomechanical Analysis ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Visually Impaired People ◽  
Impaired People ◽  
Application Fields

A diverse array of assistive technologies have been developed to help Visually Impaired People (VIP) face many basic daily autonomy challenges. Inertial measurement unit sensors, on the other hand, have been used for navigation, guidance, and localization but especially for full body motion tracking due to their low cost and miniaturization, which have allowed the estimation of kinematic parameters and biomechanical analysis for different field of applications. The aim of this work was to present a comprehensive approach of assistive technologies for VIP that include inertial sensors as input, producing results on the comprehension of technical characteristics of the inertial sensors, the methodologies applied, and their specific role in each developed system. The results show that there are just a few inertial sensor-based systems. However, these sensors provide essential information when combined with optical sensors and radio signals for navigation and special application fields. The discussion includes new avenues of research, missing elements, and usability analysis, since a limitation evidenced in the selected articles is the lack of user-centered designs. Finally, regarding application fields, it has been highlighted that a gap exists in the literature regarding aids for rehabilitation and biomechanical analysis of VIP. Most of the findings are focused on navigation and obstacle detection, and this should be considered for future applications.

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