scholarly journals Fuzzy Logic Method for the Speed Estimation in All-Wheel Drive Electric Racing Vehicles

2021 ◽  
Vol 23 (2) ◽  
pp. B117-B129
Author(s):  
Angelo Bonfitto ◽  
Stefano Feraco ◽  
Marco Rossini ◽  
Francesco Carlomagno
Keyword(s):  
Fuzzy Logic ◽  
Weighted Average ◽  
Rear Wheel ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Fuzzy Logics ◽  
Steering Angle ◽  
Wheel Drive ◽  
Vehicle Acceleration ◽  
Fuzzy Logic Method

This paper presents a method for the vehicle speed estimation with a Fuzzy Logic based algorithm. The algorithm acquires the measurements of the yaw rate, steering angle, wheel velocities and exploits a set of five Fuzzy Logics dedicated to different driving conditions. The technique estimates the speed exploiting a weighted average of the contributions provided by the longitudinal acceleration and the credibility assigned by the Fuzzy Logics to the measurements of the wheels’ speed. The method is experimentally evaluated on an all-wheel drive electric racing vehicle and is valid for the front and rear wheel drive configurations. The experimental validation is performed by comparing the obtained estimation with the result of computing the speed as the average of the linear velocity of the four wheels. A comparison to the integral of the vehicle acceleration over time is reported.

scholarly journals Assessment of Technical Condition of an Accumulator Common Rail Injector by Temperature of its Units

2021 ◽  
Vol 23 (2) ◽  
pp. B130-B138
Author(s):  
Ildar Gabitov ◽  
Andrei Negovora ◽  
Azamat Valiev ◽  
Vladimir Ilin ◽  
Danila Plotnikov ◽  
...  
Keyword(s):  
Weighted Average ◽  
Rear Wheel ◽  
Technical Condition ◽  
Vehicle Speed ◽  
Fuzzy Logics ◽  
Steering Angle ◽  
Yaw Rate ◽  
Wheel Drive ◽  
Common Rail Injector ◽  
Vehicle Acceleration

This paper presents a method for the vehicle speed estimation with a Fuzzy Logic based algorithm. The algorithm acquires the measurements of the yaw rate, steering angle, wheel velocities and exploits a set of five Fuzzy Logics dedicated to different driving conditions. The technique estimates the speed exploiting a weighted average of the contributions provided by the longitudinal acceleration and the credibility assigned by the Fuzzy Logics to the measurements of the wheels’ speed. The method is experimentally evaluated on an all-wheel drive electric racing vehicle and is valid for the front and rear wheel drive configurations. The experimental validation is performed by comparing the obtained estimation with the result of computing the speed as the average of the linear velocity of the four wheels. A comparison to the integral of the vehicle acceleration over time is reported.

scholarly journals Cyber-Physical Attack Detection and Recovery based on RNN in Automotive Brake Systems

2018 ◽  
Author(s):  
Jongho Shin ◽  
Youngmi Baek ◽  
Jaeseong Lee ◽  
Seonghun Lee
Keyword(s):  
Classification Accuracy ◽  
Weighted Average ◽  
Data Integrity ◽  
Wheel Speed ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Detection And Identification ◽  
Fault Detection And Identification

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literatures. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and cause performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

scholarly journals Cyber-Physical Attack Detection and Recovery Based on RNN in Automotive Brake Systems

2018 ◽  
Vol 9 (1) ◽  
pp. 82 ◽  
Author(s):  
Jongho Shin ◽  
Youngmi Baek ◽  
Jaeseong Lee ◽  
Seonghun Lee
Keyword(s):  
Classification Accuracy ◽  
Weighted Average ◽  
Data Integrity ◽  
Wheel Speed ◽  
Attack Detection ◽  
Cyber Attacks ◽  
Speed Estimation ◽  
Vehicle Speed ◽  
Detection And Identification ◽  
Fault Detection And Identification

The violation of data integrity in automotive Cyber-Physical Systems (CPS) may lead to dangerous situations for drivers and pedestrians in terms of safety. In particular, cyber-attacks on the sensor could easily degrade data accuracy and consistency over any other attack, we investigate attack detection and identification based on a deep learning technology on wheel speed sensors of automotive CPS. For faster recovery of a physical system with detection of the cyber-attacks, estimation of a specific value is conducted to substitute false data. To the best of our knowledge, there has not been a case of joining sensor attack detection and vehicle speed estimation in existing literature. In this work, we design a novel method to combine attack detection and identification, vehicle speed estimation of wheel speed sensors to improve the safety of CPS even under the attacks. First, we define states of the sensors based on the cases of attacks that can occur in the sensors. Second, Recurrent Neural Network (RNN) is applied to detect and identify wheel speed sensor attacks. Third, in order to estimate the vehicle speeds accurately, we employ Weighted Average (WA), as one of the fusion algorithms, in order to assign a different weight to each sensor. Since environment uncertainty while driving has an impact on different characteristics of vehicles and causes performance degradation, the recovery mechanism needs the ability adaptive to changing environments. Therefore, we estimate the vehicle speeds after assigning a different weight to each sensor depending on driving situations classified by analyzing driving data. Experiments including training, validation, and test are carried out with actual measurements obtained while driving on the real road. In case of the fault detection and identification, classification accuracy is evaluated. Mean Squared Error (MSE) is calculated to verify that the speed is estimated accurately. The classification accuracy about test additive attack data is 99.4978%. MSE of our proposed speed estimation algorithm is 1.7786. It is about 0.2 lower than MSEs of other algorithms. We demonstrate that our system maintains data integrity well and is safe relatively in comparison with systems which apply other algorithms.

scholarly journals A Torque Vectoring Control for Enhancing Vehicle Performance in Drifting

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 394 ◽  
Author(s):  
Michele Vignati ◽  
Edoardo Sabbioni ◽  
Federico Cheli
Keyword(s):  
Control Strategies ◽  
Rear Wheel ◽  
Lateral Acceleration ◽  
Vehicle Speed ◽  
Electric Motors ◽  
Sideslip Angle ◽  
Vehicle Performance ◽  
Wheel Drive ◽  
Torque Vectoring ◽  
The One

When dealing with electric vehicles, different powertrain layouts can be exploited. Among them, the most interesting one in terms of vehicle lateral dynamics is represented by the one with independent electric motors: two or four electric motors. This allows torque-vectoring control strategies to be applied for increasing vehicle lateral performance and stability. In this paper, a novel control strategy based on torque-vectoring is used to design a drifting control that helps the driver in controlling the vehicle in such a condition. Drift is a particular cornering condition in which high values of sideslip angle are obtained and maintained during the turn. The controller is applied to a rear-wheel drive race car prototype with two independent electric motors on the rear axle. The controller relies only on lateral acceleration, yaw rate, and vehicle speed measurement. This makes it independent from state estimators, which can affect its performance and robustness.

Pengembangan Aplikasi Pendukung Keputusan Pemilihan Program Studi dengan Metode Fuzzy Logic (Studi Kasus: Universitas Multimedia Nusantara)

2016 ◽  
Vol 7 (1) ◽  
pp. 12-18
Author(s):  
Joko Haryanto ◽  
Seng Hansun
Keyword(s):  
Decision Making ◽  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
System Application ◽  
Prospective Students ◽  
Fuzzy Logic Method ◽  
Index Terms ◽  
Selection Of

This paper describes the development of decision support system application to assist students who want to enter college so that no one choose the majors incorrectly. This application uses fuzzy logic method because fuzzy logic is very flexible in data which are vague and can be represented as a linguistic variable. The purpose of this application is to assist students to choose available majors at University Multimedia Nusantara which are appropriate with his/her capabilities. This application accepts five kinds of input values i.e. Mathematics, Indonesian, English, Physics, and TIK. Received input will be processed by the calculation of the system for decision-making and the application will generate output that shows how great a match for each majors. With this application, prospective students can find out where the majors that match his/her capabilities. This application has ninety nine percentage of match result accuracy. Index Terms—fuzzy logic, decision support system, UMN, selection of major

A novel IMC-FOF design for four wheel steering systems of distributed drive electric vehicles

2021 ◽  
pp. 095440702110314
Author(s):  
Yan Ti ◽  
Kangcheng Zheng ◽  
Wanzhong Zhao ◽  
Tinglun Song
Keyword(s):  
Fractional Order ◽  
Electric Vehicles ◽  
Internal Model ◽  
Rear Wheel ◽  
Internal Model Control ◽  
Steering Angle ◽  
Comparison Results ◽  
Model Control ◽  
Tracking Ability ◽  
Internal Model Controller

To improve handling and stability for distributed drive electric vehicles (DDEV), the study on four wheel steering (4WS) systems can improve the vehicle driving performance through enhancing the tracking capability to desired vehicle state. Most previous controllers are either a large amount of calculation, or requires a lot of experimental data, these are relatively time-consuming and laborious. According to the front and rear wheel steering angle of DDEV can be distributed independently, a novel controller named internal model controller with fractional-order filter (IMC-FOF) for 4WS systems is proposed and studied in this paper. The IMC-FOF is designed using the internal model control theory and compared with IMC and PID controller. The influence of time constant and fractional-order parameters which is optimized using quantum genetic algorithms (QGA) on tracking ability of vehicle state are also analyzed. Using a production vehicle as an example, the simulation is performed combining Matlab/Simulink and CarSim. The comparison results indicated that the proposed controller presents performance to distribute the front and rear wheel steering angle for ensuring better tracking capability to desired vehicle state, meanwhile it possesses strong robustness.

A fuzzy logic neural network algorithm for compressor crankshaft-rolling bearing system optimization

2021 ◽  
pp. 1-7
Author(s):  
Zheng Zhang ◽  
Jianrong Zheng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Dynamic Analysis ◽  
Weighted Average ◽  
Rolling Bearing ◽  
System Optimization ◽  
Simulation Software ◽  
Network Algorithm ◽  
Neural Network Algorithm ◽  
Bearing System

Taking the crankshaft-rolling bearing system in a certain type of compressor as the research objective, dynamic analysis software is used to conduct detailed dynamic analysis and optimal design under the rated power of the compressor. Using Hertz mathematical formula and the analysis method of the superstatic orientation problem, the relationship expression between the bearing force and deformation of the rolling bearing is solved, and the dynamic analysis model of the elastic crankshaft-rolling bearing system is constructed in the simulation software ADAMS. The weighted average amplitude of the center of the neck between the main bearings is used as the target, and the center line of the compressor cylinder is selected as the design variable. Finally, an example analysis shows that by introducing the fuzzy logic neural network algorithm into the compressor crankshaft-rolling bearing system design, the optimal solution between the design variables and the objective function can be obtained, which is of great significance to the subsequent compressor dynamic design.

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